Multi-Class classification of prostate cancer MRI images based on UCLA score using RegNetY-320 model

Abstract

Prostate cancer (PCa) is the most frequent non-skin cancer in males, and it represents a significant global healthcare problem. Another challenge is the development of an accurate classification model for the detection of PCa. As a result, developing an accurate classification model for PCa is extremely important and has potentially significant clinical implications. These models have the potential to boost the benefits of treatment while also increasing the likelihood of a patient's survival. The PCa is classified in this research based on the T2w magnetic resonance images (MRI), which are collected from the prostate dataset. In this work, a deep learning-based approach RegNetY-320 model is proposed to classify and detect the PCa. The RegNet model is a pre-trained deep transfer learning architecture, which is most commonly used for the image classification process. The proposed model is optimized using various optimizers, including Adam, AdaMax, SGD, RMSprop, Ftrl, and Nadam, and the optimized models are termed model-01 to model-06. For the evaluation, a multiclass classification system was employed to classify 845 patient records from a mpMRI dataset with a unique "UCLA" score of the ROI. Accuracy, precision, sensitivity, specificity, and the F1 score are all calculated based on the classification for the purpose of performance analysis. Finally, in accordance with the findings, the performances of the various proposed models are evaluated in order to determine their validity. When compared among proposed models, the proposed model RegNetY-320_Model-03 optimized using the RMSprop optimizer has obtained greater performance with 95.70 percent accuracy, which is the highest accuracy performance obtained in this research.