Paper Contents
Abstract
From a medical perspective, brain cancer can be considered one of the most lethal diseases due to the damage to major blood vessels and the increased risk of death. Therefore, early and accurate diagnosis is important for the best treatment of the disease. In this paper, we describe a new method for automatic problem detection based on the VGG16 neural network, which recognizes the deep structure and good image distribution. Our model involves the enhancement of MRI scan images and then the classification of images into tumor and non-tumor using transformations with VGG16. We build models that achieve satisfactory accuracy, sensitivity and specificity using large-scale MRI images and their annotations. Our results show that the VGG16 mathematical model can assist radiologists in brain diagnosis and make brain diagnosis more efficient and reliable. Additionally, we provide an overview of the possibilities of deep learning in modern medicine and the prospects for the development of medical imaging.
Copyright
Copyright © 2024 Gautam Nimase. This is an open access article distributed under the Creative Commons Attribution License.
