Diagnosis of Chronic Brain Syndrome Using Deep Learning

Abstract

Abstract: Brain tumours, which grow within the brain or spread from other secondary tumours elsewhere in the body, have been one of the top causes of death in both adults and children in recent years. Patients can benefit from more effective treatment options if cancers are diagnosed early. Traditional feature extraction approaches concentrate on either low-level or high-level features, with some hand crafted features used to bridge the gap. By encoding/combining low-level and high-level features, a feature extraction framework can be designed to close this gap without employing handcrafted features. Deep learning is extremely powerful for feature representation because it can completely describe low-level and high-level information while also embedding the feature extraction phase in the self-learning process. A computerised technique for locating and segmenting brain tumours could help doctors make faster and more accurate diagnoses. In this paper, we propose a deep learning model that uses the VGG16 and VGG19 architectures to detect and localise cancers in MRI-based pictures. The transfer learning model was able to learn froma small number of photos and achieve a test accuracy of 92 percent for detection and a mean average precision score of 90.14 percent for segmentation.