PancreaSys: An Automated Cloud-Based Pancreatic Cancer Grading System - Sehmi, Muhammad Nurmahir Mohamad; Fauzi, Mohammad Faizal Ahmad; Ahmad, Wan Siti Halimatul Munirah Wan; Chan, Elaine Wan Ling (Frontiers in Signal Processing, 2022)
Pancreatic cancer is one of the deadliest diseases which took millions of lives over the past 20 years. Due to challenges in grading pancreatic cancer, this paper presents an automated cloud-based system, utilizing a convolutional neural network Deep Learning (DL) approach on classifying four classes of pancreatic cancer grade from pathology image, into Normal, Grade I, Grade II and Grade III. This cloud-based system, named as PancreaSys, takes an input of high power field images from the web user interface, sliced them into smaller patches, made prediction and stitched back the patches before returning the final result to the pathologist. Anvil and Google Colab are used as the backbone of the system to build a web user interface for deploying the DL model in the classification of the cancer grade. This work employs transfer learning approach on a pre-trained DenseNet201 model with data augmentation to alleviate the small dataset's challenges. A 5-fold cross-validation (CV) was employed to ensure all samples in a dataset were used to evaluate and mitigate selection bias during splitting dataset into 80% training and 20% validation set. The experiments were done on three different datasets (May Grunwald-Giemsa (MGG), Hematoxylin and Eosin (H&E) and mixture of both, called Mixed dataset) to observe the model performance on two different pathology stains (MGG and H&E). Promising performance are reported in predicting the pancreatic cancer grade from pathology images, with mean f1-score of 0.88, 0.96 and 0.89 for MGG, H&E and Mixed Dataset respectively. The outcome from this research is expected to serve as prognosis system for the pathologist in providing accurate grading for pancreatic cancer in pathological images.