OPTIMIZING MRI-BASED MEDICAL DIAGNOSIS: COMPARATIVE ANALYSIS OF EFFICIENTNET PERFORMANCE WITH VARYING LEARNING RATES

Abstract

Magnetic Resonance Imaging (MRI) has emerged as a fundamental tool in the field of medical diagnostics, offering detailed insights into anatomical structures. As the demand for efficient and accurate diagnosis increases, leveraging deep learning techniques becomes imperative, among which the learning rate stands out as a pivotal factor influencing model convergence and generalization. In this research, we investigate the influence of varying learning rates on the efficacy of the EfficientNet B0 model, a cutting-edge convolutional neural network design acclaimed for its efficiency and proficiency in tasks related to image classification. Our comparative analysis unveils the profound influence of learning rates on the diagnostic accuracy and efficiency of the model. Specifically, we observe that optimal learning rates significantly enhance the convergence speed and overall performance of EfficientNet in medical image. In conclusion, this research highlighting the importance of learning rates in improving diagnostic precision and efficacy. We observed a wide range of outcomes in terms of training and validation accuracy, as well as training and validation losses. Notably, Trial 1 and Trial 2, which utilized lower initial learning rates (0.001 and 0.01, respectively), achieved higher validation accuracy compared to Trial 3, where the initial learning rate was set to 0.1. This suggests that tuning learning rates may lead to better convergence and generalization in the training process KEYWORDS MRI, EfficientNet, classification models, learning rates, medical diagnosis. INTRODUCTION Magnetic Resonance Imaging (MRI) has become prominent as an indispensable tool for visualizing internal anatomical structures and detecting pathological abnormalities with unparalleled precision, This imaging modality provides enhanced differentiation of soft tissues and the ability to visualize anatomy from multiple perspectives, rendering it particularly advantageous for the diagnosis of a broad range of medical conditions, spanning neurological disorders, musculoskeletal injuries, and oncological malignancies, [1].