Analysis of Detecting Brain Tumors Using Deep Learning Algorithms

Abstract

Digital imaging is a powerful technology used to identify illnesses and track the effectiveness of treatment. Although the use of clinical imaging is increasing exponentially, the number of specialists devoted to reviewing it is not growing as quickly. One of the leading causes of cancer-related mortality is the brain tumor, an uncontrolled growth of malignant cells in the brain region. One of the most active research issues in the field is precise brain tumor categorization, which is essential for extracting the correct medical information from magnetic resonance imaging (MRI) scans. A Wiener filter is used in the Extreme learning machine with probabilistic scaling(ELMPS)-based classification model to remove unwanted pixels from the input scan. The photos are classified using an enhanced ELMPS algorithm at the end. Two preprocessing methods, contrast enhancement and skull stripping, are used in the DBN-GWO–based classification model to improve image quality. The fuzzy means clustering (FCM) algorithm is used for picture segmentation. Finally, brain tumors are found in the input photos using an optimized deep belief network (DBN). The grey wolf optimization (GWO) technique enhances the traditional DBN classification performance. © 2024 selection and editorial matter, P. Karthikeyan, Polinpapilinho F. Katina and R. Rajagopal; individual chapters, the contributors.