Abstract
Prostate cancer is one of the most common forms of cancer in men in many countries. The survival rate can be significantly enhanced with early detection of the cancer so that appropriate intervention can be administered. In this work, a novel automated classification algorithm by fusing a number of deep learning approaches has been proposed to detect prostate cancer from ultrasound (US) and MRI images. In addition, the proposed method explains why a specific decision is made given the input US or MRI image. Several pre-trained deep learning models having customs-developed layers are added on the top of the respective pre-trained models and applied to the datasets. The best model generates a maximum accuracy of 97% on US images and 80% on MRI images of the test set. The model that produced the best classification performance was selected to use as feature extractor from the dataset to build a fusion model as a next step. To improve the models performance, especially on the MRI dataset, a fusion model is developed by combining the best performing pre-trained model as feature extractor with some other shallow machine learning algorithms (e.g., SVM, Adaboost, K-NN, and Random Forests). This fusion approach remarkably improves the performance of the system by achieving the accuracy from aforementioned 80% to 88% on the MRI dataset. Finally, the fusion model is examined by the explainable AI to find the fact why it detects a sample as Benign or Malignant Stage in prostate cancer. The proposed approach can be adopted in smart clinics or hospitals for efficient prostate cancer detection and explanation.
•Deep Learning (DL) algorithms to detect prostate cancer from Ultrasound and MRI images.•Several pre-trained DL models with customs-developed layers are adopted.•Best DL classifier was used as feature extractor from the dataset to build a fusion model.•The fusion model is examined by the explainable AI to find the fact of prostate cancer.•Proposed method was effective for efficient prostate cancer detection and explanation.