A Robust Deep Learning Model for Brain Tumor Detection and Classification Using Efficient Net: A Brief Meta-Analysis

Abstract

Accurately detecting and classifying brain tumors’, is critical for timely diagnosis and effective treatment planning. The purpose of this paper is to provide a comprehensive examination utilizing the EfficientNet family of deep learning architectures to automatically identify and categorize three forms of brain tumors from magnetic resonance imaging (MRI) scans. The primary aim of the study is to assess the performance of different EfficientNet models (ranging from EfficientNet-B0 to EfficientNet-B7) and determine their capability to achieve high accuracy in brain tumor classification. In the implementation, a diverse dataset is compiled comprising approximately 18,500 MRI images, representing various types of brain tumors. EfficientNet models are trained, validated, and tested using a combination of Softmax and ReLU activation functions, along with the Adam optimizer by employing learning rates of 0.0005 and 0.00001 for model training and optimization. The experimental results underscore the significant potential of the EfficientNet family in brain tumor classification. The findings reveal a consistent improvement in tumor detection accuracy as model complexity increases. The attained accuracies for different EfficientNet models are; 96.07% (EfficientNet-B0), 97.86% (EfficientNet-B1), 98.21% (EfficientNet-B2), 97.86% (EfficientNet-B3), 98.93% (EfficientNet-B4), 99.64% (EfficientNet-B5), 98.57% (EfficientNet-B6), and 99.64% (EfficientNet-B7), respectively. The proposed research not only validates the efficiency of the EfficientNet architecture in classifying brain tumors but also offers valuable insights into how model complexity influences classification performance. The notably high accuracy rates emphasize the clinical promise of employing deep learning methods to aid radiologists and medical experts in precise and efficient brain tumor diagnosis. Additionally, the paper's scope adds to the growing body of knowledge regarding the application of deep learning techniques to enhance medical image analysis and diagnostic capabilities.