Lung Cancer Detection using Neural Network Matlab Project Source Code

ABSTRACT

             Lung cancer prevalence is one of the highest of cancers, at 18 %. One of the first steps in lung cancer 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 cancer cells in the chest. Lung cancer 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 cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from microscopic images of biopsy. 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 microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted and classified. Neural Network method is implemented here to detection of lung cancer of lung samples.

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Signature Recognition and Verification using neural network Matlab Project Code

ABSTRACT

              The fact that the signature is widely used as a means of personal identification tool for humans require that the need for an automatic verification system. Verification can be performed either Offline or Online based on the application. However human signatures can be handled as an image and recognized using computer vision and neural network techniques. With modern computers, there is need to develop fast algorithms for signature recognition. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However human signatures can be handled as an image and recognised using computer vision and neural network techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are various approaches to signature recognition with a lot of scope of research. In this project, off-line signature recognition & verification using neural network is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified based on parameters extracted from the signature using various image processing techniques. This technique is suitable for various applications such as bank transactions, passports with good authentication results etc.

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Rice Leaf Disease Detection using Image Processing Matlab Project with Source Code

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 and classify the diseases in Rice leafs. Rice Diseases Classification comprises of two steps: first one is Detection, Extraction and Segmentation of diseases. Secondly, Feature extraction, Classification level of disease by using Support Vector Machine (SVM) classifiers respectively. The proposed system has been experimentally tested for our own dataset and results achieved are encouraging.

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Diabetic Retinopathy Detection using Convolutional Neural Network full Matlab Project Source Code

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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Hand 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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Emotion Recognition using Speech Signal Matlab Project Code

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.

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Diabetic Retinopathy Detection using Image Processing Matlab Project Code

ABSTRACT

                  Diabetes is a group of metabolic disease in which a person has high blood sugar.  Diabetic Retinopathy (DR) is caused by the abnormalities in the retina due to insufficient insulin in the body. It can lead to sudden vision loss due to delayed detection of retinopathy. So that Diabetic patients require regular medical checkup for effective timing of sight saving treatment.  This is continuous and stimulating research area for automated analysis of Diabetic Retinopathy in Diabetic patients. A completely automated screening system for the detection of Diabetic Retinopathy can effectively reduces the burden of the specialist and saves cost as well as time. Due to noise and other disturbances that occur during image acquisition Diabetic Retinopathy may lead to false detection and this is overcome by various image processing techniques. Further the different features are extracted which serves as the guideline to identify and grade the severity of the disease. Based on the extracted features classification of the retinal image as normal or abnormal is carried out.  In this paper, we have presented detail study of various screening methods for Diabetic Retinopathy. Many researchers have made number of attempts to improve accuracy, productivity, sensitivity and specificity.

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Matlab code for Fruit Disease Detection and Classification Using Image Processing

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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Brain Tumor Detection and Classification Matlab Project Source Code

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 ensemble base classifier. After classification tumor region is extracted from those images which are classified as malignant or benign. Segmentation consists of tumor extraction phases. Quantitative results show that our proposed system performed very efficiently and accurately.

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Matlab Project code for Blood Group Detection using Convolutional Neural Network (CNN)

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 Convolutional Neural Network  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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Matlab Code for Breast Cancer Detection using Neural Network || IEEE Based Project

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. The breast cancer is one among the top three cancers in American women. In United States, the American Cancer Society estimates that, 215 990 new cases of breast carcinoma has been diagnosed, in 2004. It is the leading cause of death due to cancer in women under the age of 65 . In India, breast cancer accounts for 23% of all the female cancers followed by cervical cancers (17.5%) in metropolitan cities such as Mumbai, Calcutta, and Bangalore. However, cervical cancer is still number one in rural India. Although the incidence is lower in India than in the developed countries, the burden of breast cancer in India is alarming. 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.

          Emotional disturbances are known to occur in patient's suffering from malignant diseases even after treatment. This is mainly because of a fear of death, which modifies Quality Of Life (QOL). Desai et al.,reported an immuno histo chemical analysis of steroid receptor status in 798 cases of breast tumors encountered in Indian patients, suggests that breast cancer seen in the Indian population may be biologically different from that encountered in western practice. Most imaging studies and biopsies of the breast are conducted using mammography or ultrasound, in some cases, magnetic resonance (MR) imaging . Although by now some progress has been achieved, there are still remaining challenges and directions for future research such as developing better enhancement and segmentation algorithms. 

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Matlab Code for Audio Steganography (Secrete Information hiding in Audio)

ABSTRACT

           Today’s large demand of internet applications requires data to be transmitted in a secure manner. Data transmission in public communication system is not secure because of interception and improper manipulation by eavesdropper. So the attractive solution for this problem is Steganography, which is the art and science of writing hidden messages in such a way that no one, apart from the sender and intend recipient, suspects the existence of the message, a form of security through obscurity. Audio steganography is the scheme of hiding the existence of secret information by concealing it into another medium such as audio file. The steganography application hides different types of data within a cover file. The resulting stego also contains hidden information, although it is virtually identical to the cover file.

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Matlab code for Grape Leaf Disease Detection using Image Processing

ABSTRACT

             Identification of the grape leaf disease is the main goal to prevent the losses and quality of agricultural product. In India grape fruit crop is widely grown. So disease detection and classification of grape leaf is very critical for sustainable agriculture. It’s not possible to farmer, to monitor continuously the grape disease manually. It requires the excessive processing time, tremendous amount of work, and some expertise in the grape leaf diseases. To detect and classify the grape disease we need fast automatic process so we use SVM classifier technique. This project presents mainly five stages, viz image acquisition, pre-processing, segmentation GLCM feature extraction and SVM classification. This project is proposed to benefit in the detection and classification of grape leaf disease using support vector machine (SVM) classifier.

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Matlab Code for Prostate Cancer Detection using Image Processing

ABSTRACT

          This project gives an overview of the method of detecting prostate cancer by associating Region of interest segmentation method with Support Vector Machine. Prostate cancer is commonly prevalent carcinoma detected in most of the male population. A diagnosis of prostate cancer was complicated due to unclear symptoms and involves many procedures. One of these procedures involves the study of prostate tissue biopsy to find cancer affected region. However, no boundary specified region was considered for further studies. Recent developmental techniques in the medical imaging field, especially in SVM, have paved the way for prostate carcinoma detection. The MRI image of the prostate gland is pre-processed to reduce noise effects and Region of interest is obtained with the svm and segmentation is done. The core idea of this project is to assume that every region of prostate tissue could be related to malignant or unnatural tissues.

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Matlab code for Image Enhancement using Histogram Equalization

ABSTRACT

             Digital image enhancement is one of the most important image processing technology which is necessary to improve the visual appearance of the image or to provide a better transform representation for future automated image processing such as image analysis, detection, segmentation and recognition. Many images have very low dynamic range of the intensity values due to insufficient illumination and therefore need to be processed before being displayed. Large number of techniques have focused on the enhancement of gray level images in the spatial domain. These methods include histogram equalization, gamma correction, high pass filtering, low pass filtering, homomorphic filtering, etc. Image enhancement techniques are of particular interest in photography, satellite imagery, medical applications and display devices. Producing visually natural is required for many important areas such as vision, remote sensing, dynamic scene analysis, autonomous navigation, and biomedical image analysis.

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Matlab code for Image Fusion using Wavelet Transform and PCA

ABSTRACT

            Different medical imaging techniques such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI) provide different perspectives for the human body that are important in the physical disorders or diagnosis of diseases .To derive useful information from multimodality medical image data medical image fusion has been used. In the medical field different radiometric scanning techniques can be used to evaluate and examine the inner parts of the body. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide as much details as possible for the sake of diagnosis. The objective of image fusion is to merge information from multiple images of the same image. The resultant image after image fusion is more suitable for human and machine perception and further helpful for image-processing tasks such as segmentation, feature extraction and object recognition. This project mainly presents image fusion using wavelet method for multispectral data and high-resolution data conveniently, quickly and accurately in MATLAB. Wavelet toolbox with abundant functions, provide a quick and convenient platform to improve image visibility. The work covers the selection of wavelet function, the use of wavelet based fusion algorithms on CT and MRI medical images, implementation of fusion rules and the fusion image quality evaluation. Matlab Results show that effectiveness of Image Fusion with Wavelet Transform on preserving the feature information for the test images.

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Matlab code for Handwritten Digit Recognition using Neural Network

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 Neural Network.

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