Encephalon Disease Detection and Classification Using STransform and Sine Cosine Algorithm Based Deep Convolutional Network

Abstract

Magnetic Resonance Imaging (MRI) is playing an important role in the research of neuroscience for studying brain images. It is the one of the most frequently used diagnosis tool to detect and classify abnormalities in the brain. Automatic classification and detection is a difficult and complex task for a radiologist or clinical practitioner for identification and extraction of infected tumor areas from the MRI (Magnetic Resonance Imaging).Further, classifying the type of tumor from Magnetic Resonance (MR) images also a vital part of the diagnostic system. Factors like size, shape, and position of tumor vary from different patient’s brain. Many efforts have been made for image detection and classification, but getting accurate automated techniques taken higher computational time. Motivated by the above difficulties, the research proposes Stockwell transform based segmentation technique called as Discrete Orthonormal S-Transform (DOST) to improve the performance of detection process. The DOST also utilized for feature extraction of the image. Further, a SCA (Sine Cosine Algorithm) based DCNN (Deep Convolutional Neural Network) model has been developed for classification of brain tumors into malignant (cancerous) and benign (noncancerous) category. The SCA has been utilized for weight optimization in the fully connected layer of the DCNN model. Also the different category of hidden neuron functions at the hidden layer has been tested with the new hybrid SCADCCN model and comparison results are presented. In this thesis an effort will be made to list and cover previous work of different researchers to improve the accuracy of diagnosis process.