Abstract:
Prostate cancer is the leading malignancy within the bodies of men globally and it is also
the fifth most common cause of male cancer mortality. Prostate cancer needs to be
diagnosed early in order to reduce mortality and give better treatment to concerned
individuals. Although traditional methods of diagnosing prostate cancer are
time-consuming, computer-aided diagnosis systems provide a quicker diagnosis without
any loss of accuracy. There is a very alarming need for fast, accurate, and economical
diagnostic processes of this epidemic. The main goal of this research is to recommend
an effective framework for grading classification of prostate cancer from
histopathological images with six classes with fewer errors of classification. Various
image augmentation preprocessing techniques have been utilized for improving the
quality of images, and four different augmentation techniques to enhance the dataset
size and reduce model overfitting problems. Finally, a novel ProstadeNet architecture
which is a tuned CNN tuned in structure and hyperparameters by ablation study will be
presented. The ProstadeNet model achieved an accuracy of 99.83%. To estimate the
capacity of ProstadeNet, relative experiments with five typical transfer learning models
will be conducted to find out its relative performance. K-fold validation will be used in
measuring results. Various performance measurements have been used to show the
robustness of the models.
