Brain Tumor Detection and Classification Using Neural Network Matlab Project with Source Code

ABSTRACT

          The imaging plays a central role in the diagnosis of brain tumors. An efficient algorithm is proposed in this project for brain tumor detection based on digital image segmentation. Brain tumor may be considered among the most difficult tumors to treat, as it involves the organ which is not only in control of the body. We proposed an Artificial Neural Network Approach for Brain Tumor Detection, which gave the edge pattern and segment of brain and brain tumor itself. The segmentation of brain tumors in magnetic resonance images is a challenging and difficult task because of the variety of their possible shapes, locations, image intensities. In this project it is intended to summarize and compare the methods of automatic detection of brain tumor through Magnetic Resonance Image used in different stages of Computer Aided Detection System. Brain Image classification techniques are studied. Existing methods are classically divided into region based and contour based methods. These are usually dedicated to full enhanced tumors or specific types of tumors. The amount of resources required to describe large set of data is simplified and selected in for tissue segmentation. In this project image segmentation techniques were applied on input images in order to detect brain tumors. Also in this project a Neural Network model that is based on machine learning with image and data analysis and manipulation techniques is proposed to carry out an automated brain tumor classification.

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Mr. Roshan P. Helonde

Mobile: +91-7276355704

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Email: roshanphelonde@rediffmail.com

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Real Time Face Recognition Using Matlab Project with Source Code

ABSTRACT

             The subject of face recognition is as old as computer vision because of the practical importance of the topic and theoretical interest from cognitive scientists. Despite the fact that other methods of identification (such as fingerprints, or iris scans) can be more accurate, face recognition has always remains a major focus of research because of its noninvasive nature and because it is people's primary method of person identification. This electronic document is about face detection. In computer literature face detection has been one of the most studied topics. Given an arbitrary image, the goal of this project is to determine real time face recognition. While this appears to be a trivial task for human beings, it is very challenging task for computers. The difficulty associated with face detection can be attributed to many variations in scale, location, view point, illumination, occlusions, etc. Although there have been hundreds of reports reported approaches for face detection, if one were asked to name a single face detection algorithm that has most impact in recent decades, it will most likely be the face detection, which is capable of processing images extremely rapidly and achieve high detection rates.

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Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Fruit Disease Detection and Classification Using Image Processing Matlab Project with Source Code

ABSTRACT

            Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, a solution for the detection and classification of fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following steps; in the first step K-Means clustering technique is used for the image segmentation, in the second step some features are extracted from the segmented image, and finally images are classified into one of the classes by using a Support Vector Machine. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases.

             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.

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Mr. Roshan P. Helonde

Mobile: +91-7276355704

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Email: roshanphelonde@rediffmail.com

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Image Steganography Using Matlab Project with Source Code

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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Mr. Roshan P. Helonde

Mobile: +91-7276355704

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Email: roshanphelonde@rediffmail.com

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Real Time Driver Drowsiness Detection Using Matlab Project with Source Code

ABSTRACT

             Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy and cause accidents. It is a state which they often fail to recognise early enough according to the experts. Studies show that around one quarter of all serious motorway accidents is attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drink-driving. Driver fatigue is a significant factor in a large number of vehicle accidents. The development of technologies for detecting drowsiness at the wheel is a major challenge in the field of accident avoidance systems. Because of the hazard that drowsiness presents on the road, methods need to be developed for counteracting its affects. The main aim of this is to develop a drowsiness detection system by monitoring the eyes and mouth; it is believed that the symptoms of driver fatigue can be detected early enough to avoid a car accident. Detection of fatigue involves the observation of eye movements, blink patterns and mouth opening for yawning. The analysis of face images is a popular research area with applications such as face recognition, and human identification security systems. This project is focused on the localization of the eyes, which involves looking at the entire image of the eye, and determining the position of the eyes.

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Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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