Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW-CNN Approach

Abstract

This study introduces a novel Black Widow-Optimized Dense CNN (BW-DCNN) framework for the automated detection and segmentation of brain tumors in MRI scans, aiming to enhance early diagnosis and treatment planning. With the increasing frequency of brain tumors, early and accurate detection becomes crucial for effective treatment. Magnetic Resonance Imaging (MRI) is a diagnostic technique owing to its excellent sensitivity. However, manual MRI data analysis is time-consuming and prone to errors. Leveraging advancements in Convolutional Neural Networks (CNNs) and optimization algorithms, the proposed BW-DCNN framework utilizes a unique integration of preprocessing, segmentation enhancement, and classification techniques optimized through Black Widow Optimization to improve diagnostic accuracy and efficiency. Evaluating the BW-DCNN framework against existing methodologies, including DCNN, DNN, and DBN, demonstrates superior performance across a comprehensive suite of metrics. These results highlight the potential of the BW-DCNN approach to significantly advance the capabilities of computer-aided diagnostic systems in medical imaging, offering a promising direction for future research and application in clinical settings.