Multi-organ squamous cell carcinoma classification using feature interpretation technique for explainability

• Autorzy: Prabhu, Swathi , Prasad, Keerthana , Hoang, Thuong , Lu, Xuequan , Sandhya, I.
• Języki publikacji: EN

Abstrakty

EN

Squamous cell carcinoma is the most common type of cancer that occurs in many organs of the human body. To detect carcinoma, pathologists observe tissue samples at multiple magnifications, which is time-consuming and prone to inter- or intra-observer variability. The key challenge for automation of squamous cell carcinoma diagnosis is to extract the features at low (100x) magnification and explain the decision-making process to healthcare professionals. The existing literature used either machine learning or deep learning models to detect squamous cell carcinoma of specific organs. In this work, we report on the implementation of an explainable diagnostic aid system for squamous cell carcinoma of any organ and present a comparative analysis with state-of-the-art models. A classifier with an ensemble feature selection technique is developed to provide an automatic diagnostic aid for distinguishing between squamous cell carcinoma positive and negative cases based on histopathological images. Moreover, explainable AI techniques such as ELI5, LIME and SHAP are introduced to machine learning model which provides feature interpretability of prediction made by the classifier. The results show that the machine learning model achieved an accuracy of 93.43% and 96.66% on public and multi-centric private datasets, respectively. The proposed CatBoost classifier achieved remarkable performance in diagnosing multi-organ squamous cell carcinoma from low magnification histopathological images, even when various illumination variations were introduced.

Słowa kluczowe

PL

sztuczna inteligencja; uczenie maszynowe; obraz histopatologiczny; uczenie głębokie

EN

squamous cell carcinoma; artificial intelligence; explainable machine learning; histopathological image; deep learning

• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 2
• Strony: 312--326
• Opis fizyczny: Bibliogr. 58 poz., rys., tab., wykr.

Twórcy

autor

Prabhu, Swathi

• Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India

autor

Prasad, Keerthana

• Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India,

autor

Hoang, Thuong

• School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, VIC 3220, Victoria, Australia

autor

Lu, Xuequan

• Department of Computer Science and IT, La Trobe University, Melbourne, VIC 3086, Victoria, Australia

autor

Sandhya, I.

• Department of Pathology, A J Institute of Medical Sciences and Research Center, Mangalore, 575004, Karnataka, India

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Identyfikatory

DOI

10.1016/j.bbe.2024.03.001