Image Encryption Decryption Using Arnold Transform Python Project With Source Code

         

 ABSTRACT

              Digital image scrambling can make an image into a completely different meaningless image during transformation, and during hiding information of the digital image, which also known as information disguise. Image scrambling technology depends on data hiding technology which provides non-password security algorithm for information hiding. Data hiding technology led to a revolution in the warfare of network information, because it brought a series of new combat algorithms, and a lot of countries pay a lot of attentions on this area. Network information warfare is an important part of information warfare, and its core idea is to use public network for confidential data transmission. The image after scrambling encryption algorithms is arnold, so attacker cannot decipher it.

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Plant Leaf Disease Identification Using Deep Learning Convolutional Neural Network CNN In Python Project Source Code || IEEE Based Final Year Projects

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 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 algorithm presented has three basic steps: Image Pre-processing and analysis Recognition of plant disease. 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. Once the captured image is pre-processed, the various properties of the plant leaf such as intensity, color and size are extracted and sent to with Deep Learning Convolutional  Neural Network CNN for classification. 

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Brain Tumor Detection Using Image Processing Python Project With Source Code | IEEE Based Projects

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 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 Image Processing in Python.

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Papaya Fruit Diseases Detection Using Deep Learning Neural Network (CNN) | IEEE Project | Final Year Project Source Code

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 ABSTRACT

         The diseases are a major problem faced by all the farmers including fruit farmers. It is a threat for large farmlands because these diseases spread throughout the land and make the fruits inedible, which at the end impact badly on the farmer’s income. Hence early disease detection is very important for the farmers to prevent or to control the propagation of the diseases. The traditional method of fruit disease detection and identification is naked eye observation. Even if this method is sufficient for a home gardener, it is a very inefficient one that requires experience and expertise. As a solution for this problem several computerized approaches are being developed using Machine Learning and Image Processing techniques in the resent researches. In our proposed work, we considered Papaya fruit, as it is a very popular fruit cultivation in Sri Lanka. In this study we have implemented a computerized model for papaya disease identification using Convolutional Neural Network (CNN). Among various diseases of papaya fruit, anthracnose, black spot, powdery mildew, phytophthora and ringspot were chosen. This intelligent system can easily detect the diseases and we are getting high accuracy up to 99 % to predict the papaya diseases.

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Brain Tumor Classification Using Image Processing Matlab Source Code | Final Year Project With Source Code

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 ABSTRACT

          Brain is the kernel part of the body. Brain has a very complex structure. Brain is hidden from direct view by the protective skull. This skull gives brain protection from injuries as well as it hinders the study of its function in both health and disease. But brain can be affected by a problem which cause change in its normal structure and its normal behavior .This problem is known as brain tumor. Brain tumor causes the abnormal growth of the cells in the brain. The cells which supplies the brain in the arteries are tightly bound together thereby routine laboratory test are inadequate to analyze the chemistry of brain. Brain tumor diagnosis is a very crucial task. This system provides an efficient and fast way for diagnosis of the brain tumor. Proposed system consists of multiple phases. First phase consists of texture feature extraction from brain MR images. Second phase classify brain images on the bases of these texture feature using machine learning classifier. After classification tumor region is extracted from those images which are classified as malignant or benign. Segmentation consists of tumor extraction phases.

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Top 10 M.Tech Project With Source Code | Top 10 M.E Project With Source Code | Final Year Projects Image Processing Project With Source Code

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Grape Leaf Disease Detection Using Convolutional Neural Network CNN Python Project with Source Code | Final Year Project

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 ABSTRACT

         Grapes is basically a sub-tropical plant having excellent pulp content, rich color and is highly beneficial to health. Generally, it is very time-consuming and laborious for farmers of remote areas to identify grapes leaf diseases due to unavailability of experts. Though experts are available in some areas, disease detection is performed by naked eye which causes inappropriate recognition. An automated system can minimize these problems. The disease on the grape plant usually starts on the leaf and then moves onto the stem, root and the fruit. Once the disease reaches the fruit the whole plant gets destroyed. The approach is to detect the disease on the leaf itself in order to save the fruit. In our proposed system we have used a Deep Learning model named Convolutional Neural Network. Image of the leaf is captured using the built-in camera module of a mobile phone. The accuracy achieved is 98.23% in this project.

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Melanoma Skin Cancer Detection Using CNN Convolutional Neural Network Python Project With Source Code | IEEE Project with Source Code

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 ABSTRACT

                Many of the skin diseases are very dangerous, particularly if not treated at an early stage. Skin diseases are becoming common because of the increasing pollution. Skin diseases tend to pass from one person to another. Human habits tend to assume that some Melanoma Skin Cancer are not serious problems. Sometimes, most of the people try to treat these infections of the skin using their own method. However, if these treatments are not suitable for that particular skin problem then it would make it worse. And also sometimes they may not be aware of the dangerous of their Melanoma Skin Cancer, for instance skin cancers. With advance of medical imaging technologies, the acquired data information is getting so rich toward beyond the human’s capability of visual recognition and efficient use for clinical assessment. In this project we propose a diagnosis system which will enable users to detect and recognize skin diseases with the help of image processing and provide the user advises or treatments based on the results obtained in a shorter time period than the existing methods. In this project, we will be constructing a diagnosis system based on the techniques of Image Processing. We will be making use of Python to perform the pre-processing and processing of the skin images of the users. This processing will be conducted on the different skin patterns and will be analyzed to obtain the results from which we can identify which skin disease the user is suffering from. This data will help in early detection of the skin diseases and in providing their cure. Through this we will be finding a cost effective and feasible test method for the detection of skin disorders. The results obtained will be classified according to the given prototype and diagnosis accuracy assessment will be performed to provide users with efficient and fast results.

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Image Encryption and Decryption Using AES Algorithm Python Project With Source Code | IEEE Project With Source Code

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 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 cryptography play a major role for the fulfillment for this demand. The purpose of this project is to provide the better as well as more secure communication system by enhancing the strength of Advance Encryption Standard (AES) algorithm. AES algorithm was known for providing the best security without any limitations.

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Leukemia Blood Cancer Detection Using Neural Network (CNN) Python Project Source Code || Final Year Project Codes

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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 rate 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. The proposed method is using image improvement, image segmentation for segmenting the different cells of blood, edge detection and final decision of blood cancer using Convolutional Neural Network (CNN) .

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Brain Tumor Detection Using Fuzzy C Means Algorithm Matlab Project Source Code || Final Year Project Code

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 ABSTRACT

          Brain is the kernel part of the body. Brain has a very complex structure.  Brain is hidden from direct view by the protective skull.  This skull gives brain protection from injuries as well as it hinders the study of its function in both health and disease. But brain can be affected by a problem which cause change in its normal structure and its normal behavior .This problem is known as brain tumor. Brain tumor causes the abnormal growth of the cells in the brain. The cells which supplies the brain in the arteries are tightly bound together thereby routine laboratory test are inadequate to analyze the chemistry of brain. Brain tumor diagnosis is a very crucial task. This system provides an efficient and fast way for diagnosis of the brain tumor. The method proposed in this project is fuzzy c-means (FCM) Technique.

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Top 10 Image Processing Python Project Source Code || Top 10 Final Year Projects Source Code

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COVID-19 Disease Detection Using CNN Convolution Neural Network Python Project With Source Code | Final Year Project Source Code

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 ABSTRACT

          Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this project for the diagnosis of COVID-19 based on the chest image classification. Due to the non availability of sufficient-size and good-quality chest image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts’ image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested using an independent dataset of COVID-19 images. The performance in this test scenario was as high as 99.5%. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set.

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Potato Leaf Disease Detection Using CNN Convolution Neural Network Python Project With Source Code | Final Year Project Source Code

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 ABSTRACT

          Agriculture has been an essential food source. According to related statics, over 60% of the total earth population mainly depend on agriculture’s sources for their primary feed. Unfortunately, one of the disaster problems that affect badly on agriculture production is plant diseases. There are about 25% of agriculture production lost annually because of plant diseases. Late and Early Blight diseases are one of the most destructive diseases that infect potato crop. Although, the late and inaccurate detection of plant diseases increases the losing percentage for the crop. The main approach of our proposed system is to detect early the plant diseases to decrease the plant’s production losses by using a diagnosis and detection system based on the Convolution Neural Network (CNN). We used CNN to extract the diseases features from the input images of the supported training dataset for classification purposes. For model training, 1700 of potato leaf images were used, then the testing process is done by using approximately 300 images against any biased data. Our proposed CNN architecture archives upto 99.9% accuracy, which is higher compared with other approaches run on the same dataset.

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Image Watermarking Hiding Secret Image In Cover Image Python Project With Source Code | Final Year Project

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 ABSTRACT

        Digital Image watermarking is a technology for embedding various types of information in digital content. In general, information for protecting copyrights and proving the validity of data is embedded as a watermark. A digital watermark is a digital signal or pattern inserted into digital content. The digital content could be a still image, an audio clip, a video clip, a text document, or some form of digital data that the creator or owner would like to protect. The main purpose of the watermark is to identify who the owner of the digital data is, but it can also identify the intended recipient. The Image watermarking is most popular method for copyright protection by discrete wavelet transform which performs two level decomposition of original cover image and watermark image is embedded in lowest level sub band of cover image.

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LSB Based Image Steganography Using Matlab Source Code | Hiding Secret Text In Image Using LSB

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 ABSTRACT

           Steganography is the one type of powerful technique which is science & art in which we have to write hidden messages, or we hide some important images, audio files, videos in this way that no-one, can find a hidden message which exists in cover images. Steganography is most strong techniques to mask the existence of unseen secret data within a cover object. Actually Stego means "Cover" graphy means "writing" that means It is nothing but we are hiding secret objects in cover image in which medium is different types of images. In practical feasible implementation practical approach would be to make the algorithm as strong as possible. In steganographed images are the most powerful objects that means cover objects, and therefore importance of image steganographed which can Embedding secret information inside images requires systematic computations. Various metrics were used to judge imperceptibility of steganography. The metrics in Matlab indicates how similar or dissimilar the stego-image compares with Cover.

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

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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 Deep Learning Convolutional Neural Network CNN.

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RSA Image Encryption Decryption Using Matlab Source Code | Cryptography Using RSA Algorithm

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 ABSTRACT

            Image security is an utmost concern in the web attacks are become more serious. The Image encryption and decryption has applications in internet communication, military communication, medical imaging, multimedia systems, telemedicine, etc. To make the data secure from various attacks the data must be encrypted before it is transmitting. Absolute protection is a difficult issue to maintain the confidentiality of images through their transmission over open channels such as internet or networks and is a major concern in the media, so image Cryptography becomes an area of attraction and interest of research in the field of information security. The project offer proposed system that provides a special kinds of image Encryption image security, Cryptography using RSA algorithm for encrypted images to extract using RSA algorithm. This approach provides high security and it will be suitable for secured transmission of images over the networks or Internet.

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Image Encryption Decryption Using DES Algorithm Python Project Source Code - DES Image Encryption Python Project Source Code

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ABSTRACT

            The data encryption standard is also known as DES. DES has been the most extensively used encryption algorithm standard in recent times. Encryption and decryption comprise of cryptography. Cryptography terminology is used in the data encryption standard along with standard algorithm to hide the original image. DES applies the cipher algorithm to each data block. Image encryption is being used to hide the true meaning of data so that it is very hard to attack or crack. This project deals with the simulation and synthesis results of implemented DES algorithm. Analysis of implementation is shown in step by step process. A test case is analyzed step by step to check the results at each step of the algorithm.

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Paddy Leaf Disease Detection and Pesticide Suggestion Using CNN Python Project Source Code

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ABSTRACT

           Agriculture is the main backbone for most of the developing/developed countries; agriculture production itself is the main feed for ever growing populations and it is the major source of income for the rural people/farmers especially in India. In India farmers are called “the backbone of India”. The main aim of the proposed system is to detect, classify the diseases and suggest pesticide to recover from disease in paddy leafs. Paddy leaf Diseases Classification done using Convolutional Neural Network (CNN) classifiers and then suggesting pesticide respectively. The proposed system has been experimentally tested for our own dataset and results achieved are encouraging.

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Secret Key Based Image Encryption Decryption Using AES Algorithm Python Project Source Code

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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 cryptography play a major role for the fulfillment for this demand. The purpose of this project is to provide the better as well as more secure communication system by enhancing the strength of Advance Encryption Standard (AES) algorithm. AES algorithm was known for providing the best security without any limitations.

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Prof. Roshan P. Helonde
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Matlab Code On Blood Cancer Detection Using Image Processing Full Source Code | IEEE Based Projects

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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 rate 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. The proposed method is using 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.

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Breast Cancer Detection Using CNN Convolutional Neural Network | Python Project With Source Code

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ABSTRACT

        The World Health Organization's International agency for Research on Cancer in Lyon, France, estimates that more than 150 000 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.

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Python Code On Image Watermarking for Hiding Image In Image Python Project With Source Code | Final Year Project

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ABSTRACT

        Digital Image watermarking is a technology for embedding various types of information in digital content. In general, information for protecting copyrights and proving the validity of data is embedded as a watermark. A digital watermark is a digital signal or pattern inserted into digital content. The digital content could be a still image, an audio clip, a video clip, a text document, or some form of digital data that the creator or owner would like to protect. The main purpose of the watermark is to identify who the owner of the digital data is, but it can also identify the intended recipient. The Image watermarking is most popular method for copyright protection by discrete wavelet transform which performs two level decomposition of original cover image and watermark image is embedded in lowest level sub band of cover image.

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Diabetic Retinopathy Detection Using CNN (Convolutional Neural Network) | Python Project Source Code

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 ABSTRACT

          Diabetic Retinopathy (DR) is one of the major causes of blindness in the western world. Increasing life expectancy, indulgent lifestyles and other contributing factors mean the number of people with diabetes is projected to continue rising. Regular screening of diabetic patients for DR has been shown to be a cost-effective and important aspect of their care. The accuracy and timing of this care is of significant importance to both the cost and effectiveness of treatment. If detected early enough, effective treatment of DR is available; making this a vital process. The diagnosis of diabetic retinopathy (DR) through colour fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a difficult and time consuming task. In this project , we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify Diabetic Retinopathy.

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Matlab Code for FingerPrint Recognition and Matching Using Image Processing

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ABSTRACT

            The popular Biometric used to authenticate a person is Fingerprint which is unique and permanent throughout a person’s life. A minutia matching is widely used for fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this project we projected Fingerprint Recognition using Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the boundary to preserves the quality of the image and extract the minutiae from the thinned image. Fingerprint is a very vital concept in making us completely unique and can not be altered. It is necessary to recognize fingerprint in proper manner. Here we are trying to recognize the fingerprint image samples by using minute extraction and minute matching techniques. In minute extraction it counts the crossing numbers and from the count it will be classified as normal ridge pixel, termination point and bifurcation point. Then the input finger print data is compared with the template data. This is called as minute matching. 

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Plant Disease Detection Using CNN (Convolutional Neural Network) Python Source Code

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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. Once the captured image is pre-processed, the various properties of the plant leaf such as intensity, color and size are extracted and sent to with Convolutional  Neural Network CNN for classification. 

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Lung Cancer Detection Using Convolutional Neural Network (CNN) Python OpenCV Tensorflow Project Source Code

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 ABSTRACT

             Lung cancer prevalence is one of the highest of cancers. One of the first steps in lung cancer diagnosis is sampling of lung images. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis in lung images using Convolutional Neural Network (CNN). 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 cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from lung images. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this research is to design a lung cancer detection system based on analysis of lung image using digital image processing. Lung images are feature extracted and classified for detecting lung cancer using Convolutional Neural Network (CNN).

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Cotton Leaf Disease Detection Using Deep Learning Neural Network | Python OpenCV Project Source Code

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 ABSTRACT

            Cotton is one of the most important fiber crop which is used as raw material in textile industries. But, now-a-days cotton is facing number of problems related to the healthy growth of crop due to diseases. These diseases are reducing the productivity of cotton crop and farmers are getting suffered financially due to this crop loss. Agriculture is an important source of livelihood where 65% population is depend on it. The crop loss due to disease is increasing day by day which affects on the quality and productivity of crop. As diseases on the crop are certain, the early disease detection of the crop plays major role to control the loss in agriculture. In the proposed disease detection system, the work is carried out on cotton leaves. Initially the infected region is captured and pre-processed. During segmentation, leaf as well as diseased part is segmented using thresholding clustering method. Finally classification technique is used for detecting the diseases with the help of Deep Learning Convolutional Neural Network.

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