Prostrate Cancer Classification: A Deep Learning Approach Deep Learning Model for Prostate Cancer Classification
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Abstract
Purpose: The present study was conducted to explore the performances of DL models in PET images of patients with BCR PC. The purpose of the study was to examine the efficiency of DL model in the classification of prostate cancer in normal, abnormal and discriminating by PET scans for the presence of tumor recurrence or metastases among the cancer patients.
Methods: The study was conducted in the Shaukat Khanum Cancer Hospital Peshawar. The study has included 268 random patients were examined in the process of CT/PET. The data were collected from the patients from January 2020 to October 10th 2021, 239 were found with Prostate Cancer. The data showed that 29 in 239 were found two F-fluciclovine CT/PET having median of 201+100.0 (40 to 211) days between their treatment through scans. PET images have been categorized into normal, abnormal groups with the help of their clinical reports. Convolution neural network (CNN) models were trained using two different architectures, a 2D-CNN (ResNet50) using single slices (slice-based approach) and the same 2D-CNN and a 3D-CNN (ResNet-14) using a hundred slices per PET image (case-based approach). Models’ performances were evaluated on independent test datasets.
Results: The results suggested that the 2D CNN sliced based approached were used two data sets i.e. training and test data. The sensitivity of the data set showed and confirms the presence of abnormality i.e. 91.2% (criterion of 0.531) while the curve area is found 0.862 (p-value: .00). Statistical results showed about 99% of the loss function and accuracy of the results. The results of 2D-CNN cased-based approach showed sensitivity analysis and specificity of the training data showed about 90.9% & 91.1% respectively (criterion 108.9) and the area curve was found 0.684 (p-value: .00). The results for the accuracy and loss function showed about 99.1% and 0.17% in the training data set while 88.19% and 47.64% for the test data sets.