A Comparative Study of Convolutional Neural Network Architectures for Detecting Prostate Cancer
摘要
One of the most prevalent common cancers among men is prostate cancer. Therefore, early detection is crucial for effective treatment. This study aims to detect prostate cancer using four Convolutional Neural Network (CNN) architectures. We evaluated our trained models and found a lower Root Mean Square Error (RMSE) of 2.9960 on the validation set indicating that our model can accurately detect prostate cancer in medical images. Our study suggests a promising prostate cancer detection model that could help improve patients’ early diagnosis and treatment outcomes.