A Comparative Analysis of Image Descriptors for Histopathological Classification of Gastric Cancer

• URL: http://arxiv.org/abs/2503.17105v1
• Date: Fri, 21 Mar 2025 12:46:22 GMT
• Title: A Comparative Analysis of Image Descriptors for Histopathological Classification of Gastric Cancer
• Authors: Marco Usai, Andrea Loddo, Alessandra Perniciano, Maurizio Atzori, Cecilia Di Ruberto,
• Abstract summary: Gastric cancer ranks as the fifth most common and fourth most lethal cancer globally, with a dismal 5-year survival rate of approximately 20%.<n>This study employs Machine Learning and Deep Learning techniques to classify histological images into healthy and cancerous categories.
• Score: 39.69192026190426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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