Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection

Simple Summary To enhance the automatic diagnosis of the prostate cancer using machine learning algorithm, we modify the design of convolutional neural network to support multi-scale denoising of cancer images. Transfer learning is employed to leverage the detection accuracy of the prostate cancer detection model by taking advantages from more unseen data from a source dataset. Compared to existing methodologies, our work improves the accuracy by more than 10%. Ablation studies have conducted to evaluate the contributions of the components of the proposed algorithm, with 2.80%, 3.30%, and 3.13% for image denoising, multi-scale scheme, and transfer learning, respectively. The results reveal the effectiveness of the algorithm and provide insights for five future research directions. Background: Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection. Methods: A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset. Results: Four benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively. Conclusions: The performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain.