DIAGNOSIS OF PROSTATE CANCER WITH ENHANCED EFFICIENCY USING FINE-TUNED CNN AND TRANSFER LEARNING

Abstract

Cancer is one of the high-risk diseases for humans. Prostate cases are the second most common disease in men after lung cancer, and early diagnosis is vital. Artificial intelligence technologies have begun to be used in the diagnosis of prostate cancer, and more effective and sensitive results have been obtained, preventing potential errors in human-centered methods. In this study, in order to increase the classification performance in the diagnosis of prostate cancer, transfer learning methods and fine-tuning processes, which have higher success and learning ability with less training data, unlike machine learning methods, were applied. The two-class data set consisting of prostate cancer MR images, ‘significant’ and ‘not-significant’, was classified with Alexnet, Densenet201, Googlenet, and Vgg16 models with the feature extraction approach, and 71.40%, 72.05%, 65%, and 80.13% accuracy results were obtained respectively. To increase these rates, pre-trained transfer learning models were used and accuracy results of 89.74%, 94.32%, 85.59%, and 91.05% were achieved, respectively. A 98.10% validation result was obtained using the cross-validation method in the Densenet201 model. DenseNet201 model achieved the highest accuracy result of 98.63% in transfer learning with the combination of the RMSProp optimization method. The proposed transfer learning model provided an improvement of approximately 26% compared to the feature extraction method.