MobileNetV1-Based Deep Learning Model for Accurate Brain Tumor Classification

Abstract

Brain tumors are among the most dangerous diseases that lead to mortality after a period of time from injury. Therefore, physicians and healthcare professionals are advised to make an early diagnosis of brain tumors and follow their instructions. Magnetic resonance imaging (MRI) is operated to provide sufficient and practical data in detecting brain tumors. Applications based on artificial intelligence contribute a very large role in disease detection, provide incredible accuracy and assist in creating the right decisions. In particular, deep learning models, which are a significant part of artificial intelligence, have the ability to diagnose and process medical image datasets. In this concern, one of the deep learning techniques (MobileNetV1model) is utilized to detect brain disease from 1265 images gathered from the Kaggle platform. The behavior of this model is studied through four main metrics. This article deduced that this model has a significant effect in diagnosing these images from the most important metric, which is accuracy, as it gained an accuracy result of more than 97%, which is an excellent effect.