Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques

• Published in: PeerJ. Computer science Vol. 10; p. e1996
• Main Author: Alsubai, Shtwai
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 19.04.2024; PeerJ Inc
• Subjects: Artificial Intelligence; Cancer; Care and treatment; Colon cancer; Computational linguistics; Computer Vision; Data Mining and Machine Learning; Data Science; Diagnosis; Language processing; Local binary pattern features; Lungs cancer; Machine learning; Medical imaging equipment; Methods; Natural language interfaces; Oncology, Experimental; Text Mining; Transfer learning; XAI; XAI; Colon cancer; Transfer learning; Lungs cancer; Local binary pattern features
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1996

Cancer, a life-threatening disorder caused by genetic abnormalities and metabolic irregularities, is a substantial health danger, with lung and colon cancer being major contributors to death. Histopathological identification is critical in directing effective treatment regimens for these cancers. The earlier these disorders are identified, the lesser the risk of death. The use of machine learning and deep learning approaches has the potential to speed up cancer diagnosis processes by allowing researchers to analyse large patient databases quickly and affordably. This study introduces the Inception-ResNetV2 model with strategically incorporated local binary patterns (LBP) features to improve diagnostic accuracy for lung and colon cancer identification. The model is trained on histopathological images, and the integration of deep learning and texture-based features has demonstrated its exceptional performance with 99.98% accuracy. Importantly, the study employs explainable artificial intelligence (AI) through SHapley Additive exPlanations (SHAP) to unravel the complex inner workings of deep learning models, providing transparency in decision-making processes. This study highlights the potential to revolutionize cancer diagnosis in an era of more accurate and reliable medical assessments.