Suganya Athisayamani1, A. Robert Singh2, Gyanendra Prasad Joshi3, Woong Cho4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 155-183, 2025, DOI:10.32604/cmes.2024.056129
- 17 December 2024
Abstract In radiology, magnetic resonance imaging (MRI) is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures. MRI is particularly effective for detecting soft tissue anomalies. Traditionally, radiologists manually interpret these images, which can be labor-intensive and time-consuming due to the vast amount of data. To address this challenge, machine learning, and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans. This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods. There are three… More >
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