A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer

TL;DR

This study compares various machine learning and deep learning classifiers, including a novel Transformer-based model, on gastric histopathology images, demonstrating their complementarity and potential to improve gastric cancer detection accuracy.

Contribution

It introduces a comprehensive experimental platform evaluating classical and deep learning classifiers, including a Transformer-based model, for gastric histopathology image classification.

Findings

Classical machine learning models excel in abnormal category classification.

Deep learning models show complementarity and improved ensemble performance.

Ensemble learning with selected classifiers enhances gastric cancer detection.

Abstract

Gastric cancer is the fifth most common cancer in the world. At the same time, it is also the fourth most deadly cancer. Early detection of cancer exists as a guide for the treatment of gastric cancer. Nowadays, computer technology has advanced rapidly to assist physicians in the diagnosis of pathological pictures of gastric cancer. Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning. The complementarity of sub-size pathology image classifiers when machine performance is insufficient is explored in this experimental platform. We choose seven classical machine learning classifiers and four deep learning classifiers for classification experiments on the GasHisSDB database. Among them, classical machine learning algorithms extract five different image virtual features to match multiple classifier algorithms. For deep learning, we choose three convolutional neural network classifiers. In addition, we also choose a novel Transformer-based classifier. The experimental platform, in which a large number of classical machine learning and deep learning methods are performed, demonstrates that there are differences in the performance of different classifiers on GasHisSDB. Classical machine learning models exist for classifiers that classify Abnormal categories very well, while classifiers that excel in classifying Normal categories also exist. Deep learning models also exist with multiple models that can be complementarity. Suitable classifiers are selected for ensemble learning, when machine performance is insufficient. This experimental platform demonstrates that multiple classifiers are indeed complementarity and can improve the efficiency of ensemble learning. This can better assist doctors in diagnosis, improve the detection of gastric cancer, and increase the cure rate.