Exploring the Potential of Deep Learning for Brain Tumor MRI Image Classification

Abstract

Modern medical devices use MRI images as a file format to explain the components of the human body, such as the human brain.  Successful treatment and clinical diagnosis of brain tumors rely on accurate tumor classification. A method used to categorize brain tumors in this paper, based on machine learning classifiers and deep features, is proposed. The suggested framework makes use of multiple pre-trained deep convolutional neural networks (CNN) and the transfer learning concept to extract deep features from brain MRI images. Numerous machine learning classifiers assess the retrieved deep features and the best three models of deep features are selected. Many machine learning classifiers are defined and sequenced as a set of deep features, which are then input into several ML classifiers for use in making output predictions. Using three publicly available brain MRI datasets, we compare the performance of various pre-trained models for brain tumor classification, including deep feature extractors, a deep feature ensemble, and machine learning classifiers. In most instances, a CNN is utilized, and the output of the experiments demonstrates that a collection of deep features can greatly enhance tumor detection performance. The efficiency of the proposed method's is tested by measuring accuracy, which averages of about 99.2%, denoting the ability to be used in medical applications. The proposed method could be used in all other human body components for anomaly detection