MEG AND PET IMAGES-BASED BRAIN TUMOR DETECTION USING KAPUR’S OTSU SEGMENTATION AND SOOTY OPTIMIZED MOBILENET CLASSIFICATION

Deep learning techniques have revolutionized medical image analysis recently, particularly in brain tumor (BT) detection. A comprehensive overview of the advancements and challenges associated with employing deep learning methodologies for the accurate and timely detection of BT. This work proposes a novel brain hybrid hexagonal mobile network (BH2Mnet) to identify benign and malignant tumors using MEG and PET images. An adaptive bilateral filter (ABF) is used as a de-noising for input images to eliminate noise artifacts. Eliminating the skull and outer cortical regions, a process known as "skull stripping" is utilized to enhance the number of training images. The de-noised images are segmented to detect the BT using Kapur's Otsu threshold (KOT) algorithm. Based on these segmented tumors, hexagonal feature sets with and without segmentation masks are produced using hybrid hexagonal features (HHF). Finally, the Sooty optimization-based MobileNet classifier is employed to classify the BT into benign, malignant cases. It was determined that the proposed BH2Mnet approach was 99.21 % accurate in classifying data. According to the proposed BH2Mnet NS-CNN, the total accuracy is enhanced by 1.67 %, 2.69 %, and 4.11 % compared to hybrid DAE, BFC, Deep CNN, and Neutrosophy.