IDENTITY THE EXTRACT BRAIN TUMOR VOLUME USING EDGE DETECTION ALGORITHM USING DEEP LEARNING MODELS

Abstract

Brain tumours are a potentially fatal condition characterized by the abnormal development of tissues within the brain. According to the cell types present in a tumour, it can be divided into benign and malignant categories. Benign tumours are noncancerous and possess a rudimentary size and shape. In addition, malignant tumours are considered cancerous and lack clearly defined borders. So many imaging techniques for interior body evaluation and analysis have been introduced by modern technology. Edge detection methods are implemented to diagnose numerous diseases due to their superior accuracy and quality compared to other methods. Utilizing standard approaches to diagnose brain cancers utilizing MRI and identifying their characteristics is challenging due to the complexity of the brain. Hence, image processing methods can be utilized to automatically and effectively detect and extract features from brain tumours. This work proposes a five-step approach for identifying and features extracted of brain cancers, including preprocessing, skull stripping, detecting tumours in axial, coronal, and sagittal planes, determining tumour location, and features extraction. Effectively identifying a brain tumour and its characteristics will be facilitated by the study's findings for physicians and medical technicians.