Tomato Leaf Disease Detection Using Image Processing || Python Project with Source Code

           , ,    

 ABSTRACT

          India is an agricultural country and soybean production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of tomato plant diseases is essential to detect the symptoms of tomato diseases as early as they appear on the growing stage. This project proposed a methodology for the analysis and detection of tomato plant leaf diseases using recent digital image processing techniques. In this project, experimental results demonstrate that the proposed method can successfully detect and classify the major tomato leaf diseases like Bacterial Spot, Early Blight, Leaf Curl Virus, Mosaic Virus, Healthy Leaf, etc.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
Share:

Walnut Disease Detection Using Convolutional Neural Network (CNN) || Final Year Projects

           ,    

ABSTRACT

         We present a system where image analysis studies aiming at automated detection of disease that may be present in walnut by using image processing. In this work, we propose a convolution image processing model that has been trained with images of walnut. The project's aim is to build a fully automated walnut disease detection using convolutional neural network CNN in this categories into two category one is healthy walnut and secondly walnut affected with disease. 

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Blood Group Detection Using Image Processing Matlab Source Code || IEEE Based Projects

           ,    

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 and Support Vector Machine 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. 

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Top 10 Image Processing Projects With Source Code | Top 10 Final Year Projects With Source Code

           , , , , , , ,    



Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com



Share:

Medicinal Leaf Recognition Using Image Processing Python Project Source Code || IEEE Based Projects

           , , ,    

 ABSTRACT

            Image processing is the recent growing technique in the world. It refers to the processing of digital images by means of a digital computer. Images play a major role in human perception. Image analysis is between image processing and computer vision. There are no clear boundaries for in continuum with image processing and computer vision. The useful paradigms for computerized process in determining the image is classified in to three types are low-level process: involve primitive operation such as image pre processing to reduce noise, image enhancement and image sharpening, mid-level: image segmentation and high-level: making sense of image recognized. Here image processing technique is used for medicinal purpose by extracting the features of herbal leaf and authenticating it medicinal qualities. Leaves play the major role for the classification of plants. The sample leaves are taken from various places, plants and shape. The image is captured and further work is carried out. Comparison of test sample image with reference not only requires an experienced but is subjective and prone to human errors. By applying advanced technique of image processing and utilizing the capabilities of the recent advanced computing and data/image storage facilities and the use of computer techniques for analyzing the shape, texture, color, aspect ratio, vein structure, entropy, compactness and so on. The aim of the work is to classify and authenticate the medicinal plant materials and herbs widely used for Indian herbal medicinal preparation. The quality and authenticity of these leaves are to be ensured for the preparation of herbal medicines. The medicinal plant leaves are thoroughly screened, analyzed and compared with the database to give the correct measures of the texture to which category the leaf belongs to. This method is adopted due to the mistaken of lookalike leaves using image processing technique the mistaken of look-alike leaves can be authenticated by various parameters of the leaves. 

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
Share:

DWT & DCT Based Image Compression Comparative Analysis | Matlab Project With Source Code

           , ,    

ABSTRACT

            The lossless compression is that allows the original data to be perfectly reconstructed from the compressed data. Lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that probable. The main objective of image compression is to decrease the redundancy of the image data which helps in increasing the capacity of storage and efficient transmission. Image compression aids in decreasing the size in bytes of a digital image without degrading the quality of the image to an undesirable level. Image compression plays an important role in computer storage and transmission. The purpose of data compression is that we can reduce the size of data to save storage and reduce time for transmission. Image compression is a result of applying data compression to the digital image.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Soybean Leaf Diseases Detection Using Image Processing Python Project Source Code || Final Year Projects

           , , ,    

ABSTRACT

          India is an agricultural country and soybean production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of soybean plant diseases is essential to detect the symptoms of soybean diseases as early as they appear on the growing stage. This project proposed a methodology for the analysis and detection of soybean plant leaf diseases using recent digital image processing techniques. In this project, experimental results demonstrate that the proposed method can successfully detect and classify the major soybean diseases like Soybean Rust, Powdery Mildew, Frogeye Leaf Spot, Downy Mildew, etc.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
Share:

Automated Malaria Detection In Blood Smear Images Using Image Processing || IEEE Based Projects || Final Year Projects

           , , ,    

ABSTRACT

              Malaria is an extremely infectious disease cause due to blood parasite of genus plasmodium. Malaria is a terrible disease in the hematological region causing millions of mortality; hence the fast diagnosing is the extreme requirement of era. Conventional microscopy, which is presently “the gold Standard” for malaria detection has occasionally proved ineffective as it takes lots of time and outcomes are complicated to reproduce. Since it poses a global health problem, automation of the evaluation method is of high significance. An image processing system is able to enhance outcomes of detection of malaria parasite cell. A variety of image processing techniques are used in the proposed method. The method proceeds in steps like image transformation, classification and feature extraction. This method assists to reduce time as well as afford the accuracy to detect malaria to certain extent. There are lots of methods to detect malaria, among them manual microscopy is considered to be "the gold standard". However because of the various steps essential in manual estimation, this diagnostic technique takes too much time. Malaria infections are detected manually by pathologists who observe the microscopic images of strained blood records on glass slides and calculate the contaminated blood cells. If sample size of patient is great, there is always a possibility to detect imprecisely. There is a chance to occur human error, so computer based classification using digital image processing methods gives better outcome than the manual diagnoses of Malaria. Intend of this work is to build up a detection method to correctly detected malaria parasites present in images. In the pre-processing stages digital image processing systems are used to obtain high-quality medical images. In this project, Image Processing is used to detect the existence of Malaria Parasite. In the proposed system, various steps are used such as image transformation, feature extraction and image classification.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
Share:

Audio Speech Emotion Recognition Using Matlab Project Code || IEEE Based Projects || Final Year Projects

           , , , ,    

ABSTRACT

          Emotion Recognition is a recent research topic in the field of Human Computer Interaction Intelligence and mostly used to develop wide range of applications such as stress management for call centre employee, and learning & gaming software, In E-learning field, identifying students emotion timely and making appropriate treatment can enhance the quality of teaching. Main aim of HCI is to achieve a more natural interaction between machine and humans. HCI is an emerging field using which we can improve the interactions between users and computers by making computers more respond able to the user’s needs. Today’s HCI system has been developed to identify who is speaking or what he/she is speaking. If in the HCI system, the computers are given an ability to detect human emotions then they can know how he/she is speaking and can respond accurately and naturally like humans do. In this project methodology for emotion recognition from speech signal is presented. Here, some of acoustic features are extracted from speech signal to analyze the characteristics and behavior of speech. The system is used to recognize the basic emotions. It can serve as a basis for further designing an application for human like interaction with machines through natural language processing and improving the efficiency of emotion. In this format, energy, Mel Frequency Cepstral Coefficients (MFCC) has been used for feature extraction from the speech signal. Support Vector Machine (SVM) are used for recognition of emotional states. English datasets are used for analysis of emotions with SVM Kernel functions.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Matlab Code for Iris Recognition Using Image Processing Full Project Source Code || Final Year Project || IEEE Based Project

              

ABSTRACT

             This project presents an iris coding method for effective recognition of an individual. The recognition is performed based on a mathematical and computational method. It consists of calculating the differences coefficients of overlapped angular patches from the normalized iris image for the purpose of feature extraction. Iris recognition belongs to the biometric identification. Biometric identification is a technology that is used for the identification an individual based on ones physiological or behavioral characteristics. Iris is the strongest physiological feature for the recognition process because it offers most accurate and reliable results. Iris recognition process mainly involves three stages namely, iris image preprocessing, feature extraction and template matching. In the pre-processing step, iris localization algorithm is used to locate the inner and outer boundaries of the iris. Detected iris region is then normalized to a fixed size rectangular block. In the feature extraction step, texture analysis method is used to extract significant features from the normalized iris image.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Reversible Image Steganography for Data Hiding Matlab Project Source Code

           , ,    

ABSTRACT

           Reversible Data Hiding (RDH) techniques have gained popularity over the last two decades, where data is embedded in an image in such a way that the original image can be restored. Earlier works on RDH was based on the Image Histogram Modification that uses the peak point to embed data in the image. More recent works focus on the Difference Image Histogram Modification that exploits the fact that the neighboring pixels of an image are highly correlated and therefore the difference of image makes more space to embed large amount of data. In this project we propose a framework to increase the embedding capacity of reversible data hiding techniques that use a difference of image to embed data. The main idea is that, instead of taking the difference of the neighboring pixels, we rearrange the columns (or rows) of the image in a way that enhances the smooth regions of an image. Any difference based technique to embed data can then be used in the transformed image. The proposed method is applied on different types of images including textures, patterns and publicly available images.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Liver Cancer Detection Using Image Processing Matlab Project with Source Code || IEEE Based Projects

           , , ,    

ABSTRACT

        Liver cancer is one of the most severe types ofcancerous diseases which is responsible for the death of many patients. Liver cancer images have more noises which is difficult to diagnose the level of the tumor. It is a challenging task to automatically identify the tumor from images because of several anatomical changes in different patients. The cancer is difficult to find because of the presence of objects with same intensity level. In this proposed system, fully automated machine learning is used to detect the liver tumor from input image. Region growing technique is used to segment the region of interest. The textural feature are extracted from Gray level co-occurrence matrix (GLCM) of the segmented image. Extracted textural features are given as input to the designed SVM classifier system. Performance analysis of SVM classification of liver cancer image is studied. This will be useful for physician in better automatic diagnosis of liver caner from input images.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Lung Nodule Detection Using Image Processing Matlab Project With Source Code IEEE Based Projects

           , , , ,    

ABSTRACT

        Lung nodule prevalence is one of the highest of cancers, at 18 %. One of the first steps in lung nodule diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of nodule cells in the chest. Lung nodule diagnosis using lung images. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung nodule in patients. Hence, there is need for a system that is capable for detecting lung nodule automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung nodule detection. The aim of this research is to design a lung nodule detection system based on analysis of microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted and classified using support vector machine. This method is implemented to detection of lung nodule of lung samples.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
Share:

Fruit Disease Detection Using CNN Convolutional Neural Network | Python Project With Source Code

           ,    

ABSTRACT

            Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. Fruit diseases can cause significant losses in yield and quality appeared in harvesting. For example, soybean rust (a fungal disease in soybeans) has caused a significant economic loss and just by removing 20% of the infection, the farmers may benefit with an approximately 11 million-dollar profit (Roberts et al., 2006). Some fruit diseases also infect other areas of the tree causing diseases of twigs, leaves and branches. An early detection of fruit diseases can aid in decreasing such losses and can stop further spread of diseases. A lot of work has been done to automate the visual inspection of the fruits by machine vision with respect to size and color. However, detection of defects in the fruits using images is still problematic due to the natural variability of skin color in different types of fruits, high variance of defect types, and presence of stem/calyx. To know what control factors to consider next year to overcome similar losses, it is of great significance to analyze what is being observed. In this project, Fruit Disease Detection done Using CNN Convolutional Neural Network in Python. The image processing based proposed approach is composed this project. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
Share:

Facial Expression Based Emotion Recognition Using Image Processing | Python Project Source Code | IEEE Based Project

           , , , ,    

ABSTRACT

            This project objective is to introduce needs and applications of facial expression recognition. Between Verbal & Non-Verbal form of communication facial expression is form of non-verbal communication but it plays pivotal role. It express human perspective or filling & his or her mental situation. A big research has been addressed to enhance Human Computer Interaction (HCI) over two decades. This project includes introduction of facial emotion recognition system, Application, comparative study of popular face expression recognition techniques & phases of automatic facial expression recognition system. Emotional aspects have huge impact on Social intelligence like communication understanding, decision making and also helps in understanding behavioral aspect of human. Emotion play pivotal role during communication. Emotion recognition is carried out in diverse way, it may be verbal or non-verbal .Voice (Audible) is verbal form of communication & Facial expression, action, body postures and gesture is non-verbal form of communication. While communicating only 7% effect of message is contributes by verbal part as a whole, 38% by vocal part and 55% effect of the speaker’s message is contributed by facial expression. For that reason automated & real time facial expression would play important role in human and machine interaction. Facial expression recognition would be useful from human facilities to clinical practices. Analysis of facial expression plays fundamental roles for applications which are based on emotion recognition like Human Computer Interaction (HCI), Social Robot, Animation, Alert System & Pain monitoring for patients.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
Share:

Leukemia Blood Cancer Detection Using Neural Network | Matlab Project Source Code | IEEE Based Project

           , ,    

ABSTRACT

        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 and a fuzzy inference system is use in this study as promising modalities for detection of different types of blood cancer. The accuracy rate of the diagnosis of blood cancer by using the fuzzy system 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. The proposed method is using Wavelet Transformation for image improvement, image segmentation for segmenting the different cells of blood, edge detection for detecting the boundary, size, and shape of the cells and finally Clustering for final decision of blood cancer based on the number of different cells.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

FingerNail Disease Detection Using Image Processing Matlab Project Source Code | IEEE Based Projects

           ,    

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 VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
Share:

Audio Steganography (Hiding Secret Audio In Audio) Using Matlab Project Source Code | IEEE Based Projects

           , ,    

ABSTRACT

             Steganography is one of the methods of secret communication that hides the existence of message so that a viewer cannot detect the transmission of message and hence cannot try to Extract it. It is the process of embedding secret data in the cover Audio without significant changes to the cover audio. These algorithms keep the messages from stealing, destroying from unintended users on the internet and hence provide security. In this project we perform Audio Steganography Hiding Secret audio In Cover audio using matlab.

PROJECT OUTPUT


PROJECT VIDEO
Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com

Share:

Secret Key Based Image Steganography Using Python Project Source Code - Secret Data Hiding In Image Using Python Project

           ,    

ABSTRACT

           Steganography is the process of hiding a secret message within a larger one in such a way that someone can not know the presence or contents of the hidden message. The purpose of Steganography is to maintain secret communication between two parties. Unlike cryptography, which conceals the contents of a secret message, steganography conceals the very fact that a message is communicated. In this project secret message is embedding in cover image while sending to receiver at extraction part secret message is extracted from cover image which is called as message hiding in image. Although steganography differs from cryptography, there are many analogies between the two, and some authors classify steganography as a form of cryptography since hidden communication is a type of secret message.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Brain Tumor Detection Using Convolutional Neural Network (CNN) Python Project With Source Code | IEEE Based Projects

           , , ,    

 ABSTRACT

          Brain tumors are the most common issue in children. Approximately 3,410 children and adolescents under age 20 are diagnosed with primary brain tumors each year. Brain tumors, either malignant or benign, that originate in the cells of the brain. The conventional method of detection and classification of brain tumor is by human inspection with the use of medical resonant brain images. But it is impractical when large amounts of data is to be diagnosed and to be reproducible. And also the operator assisted classification leads to false predictions and may also lead to false diagnose. Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. In this work we used Brain Tumor Detection Using Convolutional Neural Network CNN.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Finger Nail Disease Detection Using Machine Learning Full Project Source 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 VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Matlab Code for Types of Brain Tumor Detection Full Project Source Code | IEEE Based Projects

           , ,    

 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 and CT 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. 

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Medical Image Encryption Decryption Using Chaotic System Matlab Project With Source Code

           , ,    

ABSTRACT

               Due to the rapid rise of telemedicine, a lot of patients’ information will be transmitted through the Internet. However, the patients’ information is related to personal privacy, therefore, patients’ information needs to be encrypted when transmitted and stored. Medical image encryption is a part of it. Due to the informative fine features of medical images, a common image encryption algorithm is no longer applied. Common encryption algorithm has a single theory based on chaos image encryption algorithm, other encryption algorithms are based on information entropy. However, the images processed with these cipher text encryption algorithm are cyclical, the outline is clear and the anti-tamper capability is not strong. In view of the bit being the smallest measure unit of pixel, in order to overcome the weakness from above algorithm, and take the advantage of the chaotic system, this project present a chaotic medical image encryption algorithm based on bit-plane decomposition. 

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Corn Leaf Disease Detection Using Deep Learning Python Project with Source Code | IEEE Based Projects

           , , ,    

ABSTRACT

            Plant leaf diseases can affect plant leaves to a certain extent that the plants can collapse and die completely. These diseases may drastically decrease the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. A novel way of training and methodology was used to expedite a quick and easy implementation of the system in practice. The developed Python model which is able to recognize different types of Corn leaf diseases Using Deep Learning.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Image Encryption Decryption Using Elliptic Curve Cryptography (ECC) Matlab Project With Source Code

           , ,    

ABSTRACT

            During the last decade information security has become the major issue. The encrypting and decrypting of the data has been widely investigated because the demand for the better encryption and decryption of the data is gradually increased for getting the better security for the communication between the devices more privately. The Image Encryption Decryption play a major role for the fulfillment for this demand. A lot of information is perceived when we observe an image. Images have become an inevitable source of information. Everyday we come across various image from various sources. When images are confidential and we want the image to be transferred safe and securely, cryptography comes into play. The cryptographic technique which we have implemented in this project is the Elliptic Curve Cryptography (ECC). Various study on ECC has concluded that the difficultly to solve an Elliptic Curve Discrete Logarithmic Problem is exponentially hard with respect to the key size used. This property makes ECC a very good choice for encryption/decryption process compared to other cryptographic techniques which are linearly difficult or sub exponentially difficult.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Image Steganography Using Spread Spectrum Technique Matlab Project With 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. Spread spectrum is the technique of modulating a narrow band signal with a wideband signal so that the energy of the resultant signal is spread evenly across a wider spectrum and therefore hard to detect. Spread Spectrum Image Steganography (SSIS) typically modulates the binary message signal into a noise signal and the result is added to an image. This resultant signal is similar to noise. By carefully adjusting the power of the embedded signal it is possible to achieve both imperceptibility of the hidden message and reasonable recovery of the signal. The stego-images produced by SSIS will be indistinguishable from images corrupted by noise. An error correcting code is incorporated into the embedding so that the errors in signal recovery can be corrected.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

DWT and DCT Based Image Compression Comparative Analysis | Matlab Project With Source Code

           , ,    

ABSTRACT

            The lossless compression is that allows the original data to be perfectly reconstructed from the compressed data. Lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that probable. The main objective of image compression is to decrease the redundancy of the image data which helps in increasing the capacity of storage and efficient transmission. Image compression aids in decreasing the size in bytes of a digital image without degrading the quality of the image to an undesirable level. Image compression plays an important role in computer storage and transmission. The purpose of data compression is that we can reduce the size of data to save storage and reduce time for transmission. Image compression is a result of applying data compression to the digital image.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Traffic Sign Recognition Using CNN (Convolutional Neural Network) | Python Project With Source Code

           ,    

ABSTRACT

               Traffic sign recognition is an important but challenging task, especially for automated driving and driver assistance. Its accuracy depends on two aspects: feature exactor and classifier. Current popular algorithms mainly use convolutional neural networks (CNN) to execute feature extraction and classification. Such methods could achieve impressive results but usually on the basis of an extremely huge and complex network. What’s more, since the fully-connected layers in CNN form a classical neural network classifier, which is trained by conventional gradient descent-based implementations, the generalization ability is limited. The performance could be further improved if other favorable classifiers are used in python.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com

Share:

Python Code for Digit Recognition Using Image Processing Full Project Source code

           , ,    

ABSTRACT

           Handwriting recognition is one of the compelling research works going on because every individual in this world has their own style of writing. It is the capability of the computer to identify and understand handwritten digits automatically. Because of the progress in the field of science and technology, everything is being digitalized to reduce human effort. Hence, there comes a need for handwritten digit recognition in many real-time applications. Many Machine Learning and Deep Learning Algorithms are developed which can be used for this digit classification. This project performs Digit Recognition and the analysis of accuracy of algorithms Deep Learning Algorithm.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

DCT Based Image Steganography Python Project With Source Code / Hiding Text Message in Cover Image Using DCT

           ,    

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 embedded using 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.

PROJECT OUTPUT


PROJECT VIDEO

Contact:
Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
Share:

Total Pageviews

CONTACT US

Prof. Roshan P. Helonde
Mobile: +917276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com

Enter Project Title

Popular Projects

All Archive

Contact Form

Name

Email *

Message *