Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology

Olaf Hellwich, Harshita Sharma

Abstract:

Deep learning using convolutional neural networks is an actively emerging field in histological image
analysis. This study explores deep learning methods for computer-aided classification in H&E stained
histopathological whole slide images of gastric carcinoma. An introductory convolutional neural net-
work architecture is proposed for two computerized applications, namely, cancer classification based on
immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the
tissue. Classification performance of the developed deep learning approach is quantitatively compared
with traditional image analysis methods in digital histopathology requiring prior computation of hand-
crafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank
responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random
forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is com-
paratively analyzed for the corresponding classification problems. The proposed convolutional neural
network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer
classification and 0.8144 for necrosis detection.