Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

作     者：Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho 

作者机构：School of ComputingSastra Deemed to be UniversityThanjavur613401India Department of Computational IntelligenceSRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software EngineeringKangwon National UniversitySamcheok25913Republic of Korea Department of ElectronicsInformation and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea 

基　　金：We would like to thank our institutions to permit us to carry out this research. We acknowledge the patients who participated in the data collection process of the datasets used 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2025年第142卷第1期

页      码：155-183页

摘      要：In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

主 题 词：MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer 

学科分类：1305[艺术学-设计学类] 13[艺术学] 081104[081104] 08[工学] 0804[工学-材料学] 081101[081101] 0811[工学-水利类] 

核心收录：

D　O　I：10.32604/cmes.2024.056129

馆 藏 号：203155674...