A Comparative Analysis of Image Descriptors for Histopathological Classification of Gastric Cancer
Journal:
arXiv
Published Date:
Mar 21, 2025
Abstract
Gastric cancer ranks as the fifth most common and fourth most lethal cancer
globally, with a dismal 5-year survival rate of approximately 20%. Despite
extensive research on its pathobiology, the prognostic predictability remains
inadequate, compounded by pathologists' high workload and potential diagnostic
errors. Thus, automated, accurate histopathological diagnosis tools are
crucial. This study employs Machine Learning and Deep Learning techniques to
classify histopathological images into healthy and cancerous categories. Using
handcrafted and deep features with shallow learning classifiers on the
GasHisSDB dataset, we offer a comparative analysis and insights into the most
robust and high-performing combinations of features and classifiers for
distinguishing between normal and abnormal histopathological images without
fine-tuning strategies. With the RF classifier, our approach can reach F1 of
93.4%, demonstrating its validity.
