Audio Watermarking Using Matlab Project With Source Code Final Year Project

 ABSTRACT

           Currently over the millions of digital audio files such as digital songs are copied illegally during file-sharing over the networks. It has resulted as the loss of revenue for music and broadcasting industries. The traditional protection schemes are no longer useful to protect copyright and ownership of multimedia objects. These challenges have prompted significant research in digital audio watermarking for protection and authentication. Watermarking is a technique, which is used in protecting digital information like text, images, videos and audio as it provides copyrights and ownership. The identity of the owner of the audio file can be hidden in the audio file which is called Watermark. Therefore, digital audio watermarking is the process of hiding some information into the audio file in such a way that the quality and the audibility of the audio is not affected. It helps to prevent forgery and impersonation of audio signal. Audio watermarking is more challenging than image watermarking due to the dynamic supremacy of hearing capacity over the visual field. The proposed method involves Embedding and extraction of audio signal using Least Significant Bit. The audio signal which is in .wav  format undergoes segmentation, transformation and embedding the watermarked data and at the last inverse transformation will be carried out. We attempt to develop an efficient method for hiding the information in the audio file such that the copyright information will be protected from illegal copying of the information. This project is developed in matlab.

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Fruit Classification Using CNN Python Project With Source Code | Fruit Detection Using CNN Final Year Project

 ABSTRACT

              In recent years, use of image processing has been increasing day by day in different areas such as industrial image processing, medical imaging, real time imaging, texture classification, object recognition, etc. Image processing and computer vision in agriculture is another fast growing research field. It is an important analysing tool for pre-harvest to postharvest of crops. It has lots of applications in agriculture. The cultivation of crops can be improved by the technological support. The ability to identify the fruits based on the quality in food industry is very important nowadays where every person has become health conscious. There are different types of fruits available in the market. However, to identify best quality fruits is cumbersome task. Therefore, we come up with the system where fruit is detected under natural lighting conditions. The method used is texture detection method and shape detection. For this methodology, we use image processing to detect particular eight type of fruit. This fruit detection project is implemented in python using CNN convolutional neural network.

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Prof. Roshan P. Helonde

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Plant Disease Detection Using Image Processing Matlab Project | Plant Leaf Disease Classification

 ABSTRACT

            The detection and classification of plant diseases are the crucial factors in plant production and the reduction of losses in crop yield. This project proposes an approach for plant leaf disease detection and classification on plants using image processing. The plant disease diagnosis is restricted by person’s visual capabilities as it is microscopic in nature. Due to optical nature of plant monitoring task, computer visualization methods are adopted in plant disease recognition. Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires tremendous amount of work, expertise in the plant diseases, and also require the excessive processing time. Hence, image processing is used for the detection of plant diseases. Disease detection involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification. This project used for the detection of plant diseases using their leaves images in matlab platform.

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Prof. Roshan P. Helonde

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Paddy Leaf Disease Detection Using Image Processing | Final Year Matlab Project

 ABSTRACT

          Agriculture plays an important role in the economic growth of every country and so it is necessary to ensure its development. The spread of various diseases in paddy plants has increased in recent years. There is a variety of plant pathogens such as viral, bacterial, fungal and these can damage different plant parts above and below the ground. However, some abiotic factors such as water, light, radiation, temperature, humidity, atmosphere, acidity, and soil also affect the growth of the plant. Crop diseases are creating problems for farmers due to low output and economic losses and industrial agriculture. So, it is need of the hour to detect such diseases as earliest as possible. A large number of crops are grown in India which often serve as hosts to different kinds of insect pests and pathogens. Most of the Indian  regions being subtropical to  tropical, the  agro-climate is more  conducive for the development of insect pests than  disease causing  pathogens. Prevention and  early diagnosis are  critical to  limiting damage by plant pathogens. The producers need to monitor their crops and detect the first symptoms in order to prevent the spread of a plant disease, with low cost and  save the major part of the  production. Detection of leaf diseases falls important for these reasons. Identifying diseases through naked eye is often prone to high error rates and faulty classification. This project proposes a method that solves this issue  and helps in identifying and classifying  the leaf diseases by applying various image processing and convolutional neural network algorithms. Due to this complexity, even the experienced agronomists and plant pathologists are often unsuccessful to diagnose the plant  diseases accurately.  The  use  of an  automated  system  which  can detect  and  diagnose the  plant  diseases can exponentially  help  the agronomists  keep  an  eye on  the  plants  and  ensure good  health  of the  plants. This project is developed in matlab.

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Skin Cancer Detection Using Image Processing Matlab Project Code | Final Year Project

 ABSTRACT

          Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined. There have been a lot of promising recent works utilizing deep network architectures for developing automated skin lesion segmentation. Melanin is the pigment that discerns the color of human skin. The special cells produce melanin in the skin. If these cells are damaged or unhealthy, skin discoloration is visible. Skin pigment discoloration is a hazardous fact as a symptom of human skin disease with a possibility of losing natural beauty. The extracted information of the skin discoloration can work as a guide to diagnosis the disease. The image analyzing results are visually examined by the skin specialist and are observed to be highly accurate. The visual results are presented in the project. This project will generate results faster than the traditional method, making this application an efficient and dependable system for dermatological disease detection. Furthermore, this can also be used as a reliable real time teaching tool for medical students in the dermatology stream. This project is developed in matlab.

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Malaria Detection Using Image Processing Python Project | Malaria Parasite Detection Using CNN | Final Year Project

 ABSTRACT

             Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. It is preventable and curable.  Malaria is a serious disease which is caused by the parasite of the genus plasmodium. It poses a global problem and warrants an automatic evaluation process because conventional microscopy which is considered the gold standard has proven to be inefficient and its results are hard to store and reproduce. In conventional microscopy the blood of a malaria infected patient is placed in a slide and is observed under a microscope. This is a time consuming and tiring process even with the involvement of an expert technician. In this study we propose a computerized diagnosis which will help in immediate detection of the disease so that proper treatment can be provided to the malaria patient. We propose the usage of image processing techniques to automate the process of parasite detection in blood samples of patients. The proposed system is robust and it is unaffected by exceptional circumstances and achieves high percentages of accuracy. This project is develop in python.

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Image Steganography Using DCT Algorithm (Hide Text Message In Cover Image) Matlab Project Source Code

 ABSTRACT

            Steganography is the science and art of secret communication between two sides that attempt to hide the content of the message. It is the science of embedding information into the cover image without causing a loss in the cover image after embedding. Steganography is the art and technology of writing hidden messages in such a manner that no person, apart from the sender and supposed recipient, suspects the lifestyles of the message. It is gaining huge attention these days as it does now not attract attention to its information's existence. In this project the secret message is encrypted first then DCT technique is applied. Moreover, Discrete Cosine Transform (DCT) is used to transform the image into the frequency domain.  DCT algorithm is implemented in frequency domain in which the stego-image is transformed from spatial domain to the frequency domain and the payload bits are inserted into the frequency components of the cover image.

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Diabetic Retinopathy Detection Using Image Processing Matlab Project Code | Final Year Project

 ABSTRACT

          The processing of images by performing some operations in order to get enhanced images is called as image processing. It is widely used to diagnose the eye diseases in an easy and efficient manner. Several techniques has been developed for the early detection of DR on the basis of  features  such as blood. It includes the image enhancement  processes  like  histogram  equalization  and adaptive histogram equalization for the detection of DR. The persistent  damage  caused  to  the  retina  is  termed  as  the retinopathy.  The  condition  of  diabetic  retinopathy  (DR) happens  with  those  who  have  diabetes  that  results  in progressive damage to the retina.  Due to high blood glucose levels it  leads to the  damage of small blood vessels in  the retina and this may result into swelling of the retina. ie., DR is a diabetes related eye disease which occurs when the blood vessels in the retina become swelled and leaks fluid which ultimately leads to vision loss. The DR is regarded as a serious sight threatening condition. The  main  objective of  this method  is to  detect DR (Diabetic  Retinopathy)  eye  disease  using  Image  Processing techniques. The tool used  in this method is MATLAB and it is widely used in image processing. This project proposes a method for Extraction of Blood Vessels from the medical image of human  eye-retinal  fundus  image  that  can  be  used  in ophthalmology  for  detecting  DR.  This  method  utilizes  an approach  of  Adaptive  Histogram  Equalization  using  CLAHE (Contrast  Limited Adaptive  Histogram Equalization)  algorithm with Convolutional Neural Networks algorithm implementation. The result shows that affected DR is detected in fundus image and the DR  is  not  detected  in  the  healthy  fundus  image  and upto 98%  of Accuracy can be achieved in the detection of DR Project.

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Type of Skin Disease Detection Using Image Processing Matlab Project Source Code | Melanoma Disease Detection

 ABSTRACT

          Skin diseases are more common than other diseases. Skin diseases may be caused by fungal infection, bacteria, allergy, or viruses, etc. A skin disease may change texture or color of the skin. In general, skin diseases are chronic, infectious and sometimes may develop into skin cancer. The advancement of lasers and Photonics based medical technology has made it possible to diagnose the skin diseases much more quickly and accurately. But the cost of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system for dermatology at an initial stage. The extraction of features plays a key role in helping to classify skin diseases. Computer vision has a role in the detection of skin diseases in a variety of techniques. Due to deserts and hot weather, skin diseases are common in various country. We proposed an image processing-based method to detect skin diseases. This method takes the digital image of disease effect skin area, then use image analysis to identify the type of disease. This project is developed in matlab using image processing techniques with up to 98% Accuracy.

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Vegetable Leaf Recognition Using Image Processing Matlab Project Source Code | Recognition of Vegetable Leaf Using Matlab

ABSTRACT

             Leaf Recognition is now emerging for research purposes. Leaf recognition technology plays an important role in plant classification and its key issue lies in whether selected features are stable and have good ability to discriminate different kinds of leaves. It is well known that the correct way to extract plant features involves plant recognition based on leaf images. In Agriculture, vegetables plants have become an important source of energy and source of living for farmers. Correctly identifying a vegetable leaf allows farmers to differentiate between vegetables as well as a vegetable seedling and weed in the garden. With so many varieties of leafy greens coming from our local farmers each week, it can be difficult to figure out vegetable it belongs to. Though these leaves may appear similar at a glance, they are actually quite unique in terms of Shape, Texture and Color. And with the increasing use of innovative computer technology, digitalized ways have become a possibility for plant identification. The proposed system will solve the problem of determining the vegetables just through the photograph of their leaves. In particular, identification process is carried out by gathering leaves detached from the plants, treated and stained prior to the imaging. Recognition of Vegetable Leaf using Matlab project, is to create an Informative Vegetable’s Leaf Recognition using Matlab to help the farmers, botanist and Agricultural Researchers in identifying a vegetable and its common details in a convenient and reliable way. The output parameters are used to compute well documented metrics for the statistical and shape. Base on the study, the following conclusion are drawn: The system can extract various parameters from the leaf’s image that will be used in identifying Vegetable`s from the extracted leaf parameters, the system provides the statistical analysis and general information of the identified leaf.

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Diabetic Retinopathy Detection Using CNN Python Project With Source Code | Final Year Project

ABSTRACT

           Diabetic Retinopathy (DR) is a chronic health disease which requires early detection and treatment. It is important to identify DR using an intelligent system for faster prediction since manual examination and detection of the disease are unreliable and highly prone to error. Therefore, various researchers and medical experts have adopted and approached for advanced feature extraction and image classification, for early DR detection. Diabetic Retinopathy is a consequence of diabetes that affects the eyes. Damaged blood vessels in the retina, a light-sensitive tissue, are the primary cause of DR. Patients with Type 1 or Type 2 diabetes are more likely to have this condition. If the patient has a long-term case  of diabetes and  the blood sugar  level is  not regulated consistently, the odds of this  issue developing in the eye increase.  Diabetic  Retinopathy is  one  of  the most  common causes  of  blindness  in  the Western  countries. Preventing Diabetic Retinopathy has  found to be quite beneficial when people with  diabetes are  monitored regularly. This  process is  shown to be essential if Diabetic Retinopathy is discovered in its early stages due to the availability of treatment. Diabetic Retinopathy, the main cause of blindness among working-age adults, affects millions of individuals. Diabetic  Retinopathy  is  a  medical  disorder  in  which  diabetes  mellitus  causes  damage  to  the  retina.  Diabetic Retinopathy  is diagnosed  using  colored  fundus  images,  which  requires  trained clinicians  to recognize  the  presence  and importance  of  several tiny  characteristics,  making  it a  time-consuming  task.  We present  a  CNN-based  technique to  detect Diabetic Retinopathy in fundus images in this project. This project is developed in python.

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Breast Cancer Detection Using Image Processing Matlab Project With Source Code | Final Year Project Code

ABSTRACT

        The World Health Organization's International agency for Research on Cancer in Lyon, France, estimates that more than 150000 women worldwide die of breast cancer each year. Organ chlorines are considered a possible cause for hormone-dependent cancers . Detection of early and subtle signs of breast cancer requires high-quality images and skilled mammographic interpretation. In order to detect early onset of cancers in breast screening, it is essential to have high-quality images. Radiologists reading mammograms should be trained in the recognition of the signs of early onset of, which may be subtle and may not show typical malignant features. Mammography screening programs have shown to be effective in decreasing breast cancer mortality through the detection and treatment of early onset of breast cancers. In this project we have used image processing for detection of breast cancer like Benign Cancer, Malignant Cancer and Normal Breast.

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Ulcer Detection Using Image Processing Matlab Project With Source Code | Final Year Project

 ABSTRACT

            Ulcer is one of the most common indications of many serious diseases in the human digestive tract. Especially for ulcers in the small intestine where other methods may not display properly, capsule endoscopy is increasingly used in the diagnosis and clinical management. Since endoscopy generates lots of images of the entire inspection process, computer-aided detection ulcer is considered an essential relief for clinicians. In this work, a computer added design system is proposed for fully automated computer in two stages to detect images ulcer. In the first step, a detection method based on the effective prominence super pixel multilevel outline representation candidates proposed ulcer. To find the perceptual and semantically meaningful salient regions, the first image segment in multilevel super pixel segmentation. Each level corresponds to different initial sizes of super pixels region. Then the corresponding prominence according to the characteristics of color and texture of each level super pixel region is evaluated. This project is developed in matlab.

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Audio Steganography Hiding Text In Audio Matlab Project With Source Code | Final Year Project Code

 ABSTRACT

             Steganography is one of the best data hiding technique in the world which can be used to hide data without its presence felt. In today’s digital world most of us communicate via use of electronic media or internet. Most people among us remain unaware about the data loss or data theft which can happen on online transmission of data or message. Valuable information including personal data, messages transmitted through internet is vulnerable to hackers who may steal or decrypt our data or messages. This poject is about enhancing the data or message security with use of Audio Steganography using LSB algorithm to hide the message into multiple audio files. The message hidden by this application is less vulnerable to be stolen than other similar applications. This is due to following reasons: Firstly files are taken to hide high amount of message which enhance information hiding capacity. Secondly before being hidden, the message is broken into parts and shuffled randomly based on permutation generated at runtime so even if the Least Significant Bit gets encountered the message is still unarranged and meaningless which enhances its security. This application is capable to carry large amount of information with greater security. As audio steganography uses audio as a cover medium, similarly this application too uses an audio as a platform for hiding the message. User provides input message in the form of text and chooses the audio wave file to hide the message. This application provides a smart and interactive interface for message hiding and its retrieval. Message is shuffled in random sequence before being hidden. Random sequence which is generated based on certain factors is used to shuffle the message before hiding it. This further enhances the data security. 

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Image Watermarking Using DWT Algorithm Matlab Project With Source Code | Final Year Project Code

ABSTRACT

          The use of Internet technology has led to the availability of different multimedia data in various formats. The unapproved customers misuse multimedia information by conveying them on various web objections to acquire cash deceptively without the first copyright holder’s intervention. Due to the rise in cases of COVID-19, lots of patient information are leaked without their knowledge, so an intelligent technique is required to protect the integrity of patient data by placing an invisible signal known as a watermark on the medical images. In this project image watermarking is proposed using discrete wavelet transform algorithm on both standard and medical images. The project addresses the use of digital rights management in medical field applications such as embedding the watermark in images. The various quality parameters are used to figure out the evaluation of the developed method. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Skin Disease Detection Using Image Processing Matlab Project With Source Code | Melanoma Detection Final Year Project Code

ABSTRACT

         Skin cancer also known as melanoma it is one of the deadliest form of cancer if not recognized in time. Since the pigmented areas/moles of the skin can be nicely observed by simple, non-invasive visual inspection the clinical protocols of its recognition also consider several visual features. Melanoma is the deadliest form of skin cancer, which is considered one of the most common human malignancies in the world. Early detection of this disease can affect the result of the illness and improve the chance of surviving. The tremendous improvement of deep learning algorithms in image recognition tasks promises a great success for medical image analysis, in particular, melanoma classification for skin cancer diagnosis. Activation functions play an important role in the performance of deep neural networks for image recognition problems as well as medical image classification. Melanin is the pigment that discerns the color of human skin. The special cells produce melanin in the skin. If these cells are damaged or unhealthy, skin discoloration is visible. Skin pigment discoloration on cheeks is a hazardous fact as a symptom of human skin disease with a possibility of losing natural beauty. The extracted information of the skin discoloration can work as a guide to diagnosis the disease. In this project different imaging techniques like preprocessing method, segmentation and classification operations are used to analyze and extract the information of cheek’s discoloration lesion by measuring the pixel number of lesion on skin. This project is developed in matlab.

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Acne Disease Detection Using CNN Python Project With Source Code | Acne Disease Detection Using Image Processing

 ABSTRACT

          Acne is a chronic skin disease occurring from inflammation of pilosebaceous units which are hair follicles under skin and their surrounding sebaceous gland (fatty gland) clog up. Currently, dermatologist has to manually mark a location of acnes on the sheet, then count to quantify and measure treatment progress. This is an unreliable and inaccurate method. Moreover, this method requires dermatologist’s excessive effort. In this project, a novel automatic acne disease detection using Image processing technique is proposed. Acne causes significant physical and psychological problems for patients such as permanent scarring, depression and anxiety from poor self-image. When you have acnes, go to see dermatologist early is the safest way to heal and prevent future permanent scars. Acne can be caused by many factors such as overactive oil glands that produce too much oil, combine with skin cells to make pores in the skin, become plugged and p-acne bacteria cause acne disease. This project is developed in python.

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Leukemia Detection Using Image Processing Matlab Source Code | Leukemia Blood Cancer Detection Final Year Project Code

ABSTRACT

             Leukemia Blood cancer is the most prevalent and it is very much dangerous among all type of cancers. Early detection of blood cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose blood cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the technician. To obviate these problems, image processing techniques is use in this study as promising modalities for detection of Leukemia blood cancer. The accuracy of the diagnosis of blood cancer by using image processing will be yield a slightly higher rate of accuracy then other traditional methods and will reduce the effort and time. We first discuss the preliminary of cell biology required to proceed to implement our proposed method. This project presents a new automated approach for blood cancer detection and analysis from a given photograph of patient’s cancer affected blood sample. This project is developed in matlab.

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Types Of Blood Group Detection Using Image Processing Matlab Project With Source Code || Final Year Project Code

 ABSTRACT

          Determining of blood types is very important during emergency situation before administering a blood transfusion. Presently, these tests are performed manually by technicians, which can lead to human errors. Determination of the blood types in a short period of time and without human errors is very much essential. A method is developed based on processing of images acquired during the slide test. The image processing techniques such as Pre-processing, Segmentation, Thresholding, Morphological operations are used. The images of the slide test are obtained from the pathological laboratory are processed and the occurrence of agglutination are evaluated. Thus the developed automated method determines the blood type using image processing techniques. The developed method is useful in emergency situation to determine the blood group without human error. The slide test consists of the mixture of one drop of blood and one drop of reagent, being the result interpreted according to the occurrence or not of agglutination. The combination of the occurrence and non occurrence of the agglutination determines the blood type of the patient. This project is developed in matlab.

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Brain Tumor Types Detection Using CNN Python Project With Source Code | Final Year Project Codes

 ABSTRACT

            A tumor can be defined as a mass which grows without any control of normal forces. Real time diagnosis of tumors by using more reliable algorithms has been an active of the latest developments in medical imaging and detection of brain tumor in MR scan images. Hence image segmentation is the fundamental problem used in tumor detection. Image segmentation can be defined as the partition or segmentation of a digital image into similar regions with a main aim to simplify the image under consideration into something that is more meaningful and easier to analyze visually. Brain tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Magnetic Resonance Image (MRI) is the commonly used device for diagnosis. In MR images, the amount of data is too much for manual interpretation and analysis. During the past few years, brain tumor segmentation in Magnetic Resonance Imaging(MRI) has become an emergent research area in the field of medical imaging system. Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. Image processing is an active research area in which medical image processing is a highly challenging field. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions from the medical images. 

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Fruit Recognition Using Image Processing Maltab Project With Source Code Final Year Project Code

 ABSTRACT

          The ability to identify the fruits based on the quality in food industry is very important nowadays where every person has become health conscious. There are different types of fruits available in the market. However, to identify best quality fruits is cumbersome task. Therefore, we come up with the system where fruit is detected under natural lighting conditions. The method used is texture detection method and shape detection. For this methodology, we use image processing to detect particular eight type of fruit. This fruit detection project is implemented in matlab using image processing.

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Prof. Roshan P. Helonde

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Plant Disease Detection Using Image Processing Matlab Project With Source Code | Final Year Project Code

ABSTRACT

            The detection and classification of plant diseases are the crucial factors in plant production and the reduction of losses in crop yield. This project proposes an approach for plant leaf disease detection and classification on plants using image processing. The plant disease diagnosis is restricted by person’s visual capabilities as it is microscopic in nature. Due to optical nature of plant monitoring task, computer visualization methods are adopted in plant disease recognition. The aim is to detect the symptoms of the disease occurred in leaves in an accurate way. Diseases decrease the productivity of plant. Which restrict the growth of plant and quality and quantity of plant also reduces. Image processing is best way for detecting and diagnosis the diseases. In which initially the infected region is found then different features are extracted such as color, texture and shape. Finally image processing technique is used for detecting the diseases. 

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Prof. Roshan P. Helonde

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Image Encryption and Watermarking Using DWT Matlab Project With Source Code | Final Year Project Code

ABSTRACT

          The use of Internet technology has led to the availability of different multimedia data in various formats. The unapproved customers misuse multimedia information by conveying them on various web objections to acquire cash deceptively without the first copyright holder’s intervention. Due to the rise in cases of COVID-19, lots of patient information are leaked without their knowledge, so an intelligent technique is required to protect the integrity of patient data by placing an invisible signal known as a watermark on the medical images. In this project encryption and watermarking is proposed using discrete wavelet transform algorithm on both standard and medical images. The project addresses the use of digital rights management in medical field applications such as encrypting and embedding the watermark in medical images. The various quality parameters are used to figure out the evaluation of the developed method. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Bone Fracture Detection Using Image Processing Python Project With Source Code | Final Year Project Code

ABSTRACT

         Analysis of medical images plays a very important role in clinical decision making. For a long time it has required extensive involvement of a human expert. However, recent progress in data mining techniques, especially in machine learning, allows for creating decision models and support systems that help to automatize this task and provide clinicians with patient-specific therapeutic and diagnostic suggestions. In this project, we describe a study aimed at building a decision model (a classifier) that would predict the type of treatment (surgical vs. non-surgical) for patients with bone fractures based on their X-ray images. We consider two types of features extracted from images (structural and textural) and used them to construct multiple classifiers that are later evaluated in a computational experiment. Structural features are computed by applying the Hough transform, while textural information is obtained from Gray-level occurrence matrix. In research reported by other authors structural and textural features were typically considered separately. Our findings show that while structural features have better predictive capabilities, they can benefit from combining them with textural ones. This project is developed in python.

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Medical Image Encryption Decryption Matlab Project With Source Code | Medical Data Encryption and Decryption

 ABSTRACT

             In recent years mostly all the health centers and hospitals use the wireless networks and internet for biomedical information exchanging, the secure of this information in not verified and cannot be grantee in such environment, the personality of patient and for security concerns inside such institutions there is a need for encryption system that can easily encrypt the biomedical data and it can beshared with other centers via internet without and concerns about privacy. Our system based on advanced encryption standard with encryption and decryption taking to consideration the criticality of data that been encrypted. Medical image security is very important issue in new world technologies with the internet of things revolution everything is connected to the internet and need to protected and authenticated, our project can encrypt the medical images for popular people or the critical situation patient that can help to protect the patients privacy by merging many techniques.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Skin Disease Detection Using Image Processing CNN | Skin Disease Classification Using CNN Python Project With Source Code

ABSTRACT

              Skin diseases are hazardous and often contagious, especially melanoma, eczema, and impetigo. These skin diseases can be cured if detected early. The fundamental problem with it is, only an expert dermatologist is able to detect and classify such disease. Sometimes, the doctors also fail to correctly classify the disease and hence provide inappropriate medications to the patient. Our research proposes a skin disease detection method based on CNN Image Processing Techniques. Our system is Personal Computer based so can be used even in remote areas. The patient needs to provide the image of the infected area and it is given as an input to the application. Image Processing cnn techniques process it and deliver the accurate output.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +917276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Fingernail Disease Detection Using Image Processing Matlab Project Source Code | Final Year Project Code

ABSTRACT

          This project gives idea to predict diseases using the color of the nail at early stage of diagnosis. The main aim of our project is to analyze the disease without causing harm to humans. In earlier traditional system of disease detection, doctors observe the nails of patients and will predict the disease. Many diseases can be identified by analyzing nails of patients. But it is difficult for human eyes to differentiate the slight changes in color. So it is less accurate and time consuming. Our proposed system can be quite useful to overcome this issue since it is fully computer based. The input to the proposed system is image of nail. The system will process the nail image and will extract the nail’s features to diagnose the disease. Human nail consist of various features, our proposed system uses nail color changes to diagnose the disease. Here, first training set data is prepared from nail images of patients with specific diseases. This training data set is compared with extracted feature from input nail image to obtain the result. In our experiment, we found that training set data are correctly matched with color feature of nail image results. It is focused on the system of image recognition on the basis of color analysis. The proposed system is based on the algorithm which automatically extracts only nails area from scanned back side of palm (Region of Interest). These selected pixels are processed for further analysis using median filters. The system is fully computer based, so even small discontinuities in color values are observed, and we can detect color changes in the initial stage of disease.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Alzheimer's Detection Using Image Processing | Alzheimers Disease Detection Matlab Project With Source Code | Final Year Project

ABSTRACT

            Early detection of Alzheimer's disease (AD) is important so that preventative measures can be taken. Current techniques for detecting AD rely on cognitive impairment testing which unfortunately does not yield accurate diagnoses until the patient has progressed beyond a moderate AD. Alzheimer's disease considered being one of the acute diseases that cause the human death especially in people above 60 years old. Many computer-aided diagnosis systems are now widely spread to aid in Alzheimer diagnosis. Therefore, an automated and reliable computer-aided diagnostic system for diagnosing and classifying the brain diseases has been proposed. MRI (Magnetic resonance Imaging) is one source of brain diseases detection tools, but using MRI in Alzheimer classification is considered to be difficult process according to the variance and complexity of brain tissue. This project presents a survey of the most famous techniques used for the classification of brain diseases based on MRI. The Alzheimer detection and classification systems consist of four stages, namely, MRI preprocessing, Segmentation, Feature extraction, and Classification respectively. In the first stage, the main task is to eliminate the medical resonance images (MRI) noise which may cause due to light reflections or any inaccuracies in the imaging medium. The second stage, which is the stage where the region of interest is extracted (Alzheimer region). In the third stage, the features related to MRI images will be obtained and stored in an image vector to be ready for the classification process and finally the fourth stages, where classifier will take place to specify the Alzheimer kind.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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