Abstract:
Prostate cancer is the second most aggressive type of cancer diagnosed in men, seriously
affecting people’s life and health. Prostate cancer detection and grading in advance is a very critical
step for pathologists. Large scale inter observer reproducibility exists in staging the prostate
biopsies which leads us to move towards a Computer based model, which could accurately detect
and grade the cancerous prostate. Due to recent development in the field of digital pathology,
tissue microarrays (TMA) images are generated from whole slide images resulting in less
computational procedures and achieve good performance. This thesis is focused on deep learning
model to automatically stage the cancer instead of feature engineering based models Deep learning
models directly learn the features via convolutional layers and achieve good accuracy as compared
to feature engineering based models. We have used two datasets, Harvard dataset and Gleason
Challenge 2019, for implementation of our proposed model. Our proposed UNET based
architecture is used for training as well as testing and evaluation. We have used different deep
learning models for our UNET based encoder and achieved 0.728 and 0.732 average Cohen’s
kappa with F1 on both datasets respectively. The results show that our proposed UNET based deep
learning model performs better as compared to other state of art models. Hence, it would help
pathologists to automatically grade the prostate biopsies with high accuracy.
