Deep Learning-Based Brain Tumor Detection

Abstract

One of the main causes of death in the globe is brain tumors. Medical image has recently advanced significantly in both case methodologies and applications, enhancing its effectiveness in healthcare management. The brain tumor and pancreatic tumor databases, yield the most accurate and comprehensive results and are crucial resources in medical research. In terms of efficiency, precision, creativity, and other factors, these strategies improved performance. This dataset was preprocessed before being used to assess how well deep learning models identified and categorized brain cancers. Gliomas of low grade, categorized as grades I and II are often treatable through complete surgical removal. Conversely, grade I gliomas of high grade III and IV usually necessitate additional treatment with radiation. The accuracy of the proposed model yields highly effective results, achieving a performance of 96% accuracy. Secondly, the tumor is classified using an enhanced thresholding method informed by the binomial mean, variance, and standard deviation. To highlight the performance of the suggested framework and the novelty of the method are rigorously contrasted with accepted techniques. On the other hand, both geometric features and four texture attributes are obtained. These features are then combined using a step-by-step process, and the optimal features are selected using a Genetic Algorithm (GA).