Multiclass Colorectal Cancer Histology Images Classification Using Vision Transformers
Magdy Abd-Elghany Zeid, Khaled El-Bahnasy, S. E. Abo-Youssef
Abstract
Colorectal cancer (CRC) is the third most diagnosed cancer form globally and the second leading cause of cancer-related death after lung cancer. A precise histological categorization of CRC tissue is critical for diagnosis and patient management decisions. However, the variety of tissue patterns in CRC histological images makes the classification a challenging problem. This study applies Vision Transformers, a new class of deep-learning models in computer vision, to perform a multiclass tissue classification of a publicly available CRC histology images dataset. The data set consists of 5000 images with eight categories of tissue. We trained two variants of Transformers, namely Vision Transformer and Compact Convolutional Transformer, and achieved 93.3% and 95% accuracy, respectively. Our results outperform the original paper (87.4%) on the same dataset. Furthermore, our study highlights the opportunities of using Transformers in the histopathological image domain.