Enhancement in Medical Imaging Deep Learning Based Tumor Segmentation and Classification on Brain MRI

Abstract

Medical imaging has made significant progress by incorporating deep learning techniques newlineto accurately segment and classify brain tumors in Magnetic Resonance Imaging newline(MRI). This innovative method utilizes the potential of deep learning, a specific newlinebranch of Machine Learning (ML), to improve the detection and examination of brain newlinetumors in MRI. The procedure consists of two essential stages accurate tumor segmentation, newlinewhich entails delineating the borders of the tumor within the images, and newlinesubsequent classification based on the obtained information. This technique seeks to newlineenhance the accuracy and effectiveness of tumor detection by incorporating advanced newlineDeep Learning (DL) algorithms in the processing of brain MRI. This discovery has the newlinepotential to greatly influence neurosurgeons by providing more precise diagnoses and newlineimproved treatment approaches for neurological diseases. newlineThis thesis introduces novel methodologies for the detection and division of tumors newlinein MRI. A pioneering framework has been developed to enhance the accurate identification of cancers in brain MRI scans, with a specific focus on malignant brain tumors. newlineUsing deep learning techniques, the framework includes important steps like feature newlineextraction, major feature selection, and aberrant growth recognition. This makes it possible newlineto get very clear MRI with less loss and error. This represents notable progress in newlinethe industry, offering enhanced precision in detecting cancers within brain MRI scans newlineand showing potential for better diagnostic accuracy and more efficient treatment planning newlinefor malignant brain tumors. newlineThe initial research presents a neural network framework named Deep Convolution newlineNeural Network (DCNN), which employs advanced deep learning methods to identify newlineand categorize intensity in normal and abnormal regions. Moreover, the use of the newlineMulti-Layer CNN (MLC) algorithm is demonstrated to localize specific regions containing unhealthy cells within the image. The study also introduces an experiment with newlinemultiple splits in traini