Brain Tumor Detection Using Deep Neural Networks

Abstract

After cardiovascular disease, cancer is the second major cause of death. Brain tumors really do have the lowest overall survival rate of any type of cancer. Brain tumors are classified according to their morphologically, and location. Appropriate diagnosis of the Tumor type enables the physician to make the best treatment sensible decision and potentially save the patient’s life. In the domain of Artificial Intelligence, there is a critical need for a Computer-Aided Diagnosis (CAD) system that can assist physicians and radiologists with diagnosing and classification of cancers. The most powerful and common Machine learning models used for different image analysis tasks like 3D analysis, image retrieval, image classification, and object detection are known as Deep Neural Networks (DNNs). They have achieved a performance level near the human level. Based on the success of DNNs on natural images (e.g., captured images from natural scenes like Imagenet and Cifar10), they have become very popular for tasks such as medical image processing, organ/landmark localization, diagnosis of Cancer, diabetic retinopa thy detection, and Covid19 identification. In this study, a novel methodology will be proposed for the early diagnosis and classification of Brain tumors using the different models of DNNs and transfer learning.