Matlab Code for Plant Disease Detection & Classification using Neural Network

ABSTRACT

            The detection and classification of plant diseases are the crucial factors in plant production and the reduction of losses in crop yield. This paper proposes an approach for leaf disease detection and classification on plants using image processing. The algorithm presented has three basic steps: Image Pre-processing and analysis, Feature Extraction and 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 Neural Network for classification. 

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Mr. Roshan P. Helonde

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Blood Group Determination Using Image Processing Matlab Project with Source 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 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. 

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Mr. Roshan P. Helonde

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Bone Fracture Detection using Image Processing Matlab Project with source 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 automatise 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.

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Mr. Roshan P. Helonde

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Audio Steganography for Data Hiding Matlab Project with Source Code

ABSTRACT

          Information security is one of the most important factors to be considered when secret information has to be communicated between two parties. Cryptography and steganography are the two techniques used for this purpose. Cryptography scrambles the information, but it reveals the existence of the information. Steganography hides the actual existence of the information so that anyone else other than the sender and the recipient cannot recognize the transmission. In steganography the secret information to be communicated is hidden in some other carrier in such a way that the secret information is invisible. In this project an audio steganography technique is proposed to hide audio signal in image in the transform domain using wavelet transform. The audio signal in any format wav is encrypted and carried by the image without revealing the existence to anybody. When the secret information is hidden in the carrier the result is the stego signal. In this work, the results show good quality stego signal and the stego signal is analysed for different attacks. It is found that the technique is robust and it can withstand the attacks. The quality of the stego signal is measured by Peak Signal to Noise Ratio (PSNR), Mean Square Error. The quality of extracted secret audio signal is measured by Signal to Noise Ratio (SNR), Squared Pearson Correlation Coefficient (SPCC). The results show good values for these metrics.

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Mr. Roshan P. Helonde

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Brain Tumor Detection using Rough Set Theory Algorithm Matlab Project with Source Code

ABSTRACT

              Brain tumor is a life threatening disease and its early detection is very important to save life. The tumor region can be detected by segmentation of brain Magnetic Resonance Image (MRI). Once a brain tumor is clinically suspected, radiologic evaluation is required to determine the location, the extent of the tumor, and its relationship to the surrounding structures. This information is very important and critical in deciding between the different forms of therapy such as surgery, radiation, and chemotherapy. The segmentation must be fast and accurate for the diagnosis purpose. Manual segmentation of brain tumors from magnetic resonance images is a tedious and time-consuming task. Also the accuracy depends upon the experience of expert. Hence, the computer aided automatic segmentation has become important. MRI scanned images offer valuable information regarding brain tissues. MRI scans provide very detailed diagnostic pictures of most of the important organs and tissues in our body. It is generally painless and noninvasive. It does not produce ionizing radiation. So MRI is one of the best clinical imaging modalities. Several automated segmentation algorithms have been proposed. But still segmentation of MRI brain image remains as a challenging problem due to its complexity and there is no standard algorithm that can produce satisfactory results. The aim of this research work is to propose and implement an efficient system for tumor detection and classification. The different steps involved in this work are image pre-processing for noise removal, feature extraction, segmentation and classification.

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Mr. Roshan P. Helonde

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Face Recognition Based Attendance Maintenance Matlab Project with Source Code

ABSTRACT

               In this project we are proposing an automatic attendance system which can be used in every organisation to mark the attendance of employees. The main application of Automatic attendance system is seen in teaching institutions, where the attendance of students has to be regularly monitored on daily basis. The method developed provides a secure and effective may recording attendance. Automatic face recognition (AFR) technologies have made many improvements in the changing world. Smart Attendance using Real-Time Face Recognition is a real-world solution which comes with day to day activities of handling student attendance system. Face recognition-based attendance system is a process of recognizing the students face for taking attendance by using face biometrics based on high - definition monitor video and other information technology. In my face recognition project, a computer system will be able to find and recognize human faces fast and precisely in images or videos that are being captured through a surveillance camera.

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Mr. Roshan P. Helonde

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Matlab Code for Osteoporosis Detection using Image Processing Matlab Project with source code

ABSTRACT

               Osteoporosis is a progressive bone disease that is characterized by a decrease in bone mass and density which can lead to an increased risk of fracture. Osteoporosis is a state of having brittle and fragile bone which arises due to vitamin deficiency, tissue loss, hormonal changes. Osteoporosis can be efficiently detected by calculating various features like Bone mineral density (BMD), statistical features from various trabecular region such as hip, toe, elbow, etc. Detection of bone disorders are done with the help of bone densitometer. The bone densitometer uses a technique that the bone density can be measured in-terms of Tscore. Osteoporosis is a condition in which the bone becomes porous and fragile due loss in bone mineral density and gets more susceptible to fracturing. osteopenia refers to early signs of bone loss that can turn to osteoporosis. Both osteoporosis and osteopenia are increasingly found in aging women who have attained their menopause. The symptoms of osteoporosis include pain in the bones, or lower back, bone fracture and

loss of height over a course of time.

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Mr. Roshan P. Helonde

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Email: roshanphelonde@rediffmail.com

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Traffic Sign Recognition using Image Processing Matlab Project Code

ABSTRACT

          The main objective of this project is to develop an algorithm so that we can automatically recognise traffic signs. This work uses basic image processing technique for automatically recognising two different traffic signs- stop sign and yield sign. The proposed method detects the location of the sign in the image, based on its geometrical characteristics and recognizes it using colour information. Firstly thresholded on RGB domain to separate out the regions with red color, which is those traffic signs usually have, then we do region mapping due to which the rest of the parts which are too small or too large are removed since they are unlikely to be a traffic sign. Here we get the signs whose shapes are octagon or triangular thus major axis to minor axis ratio is one. Hence the regions which are very large are eliminated.

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Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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