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

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. Th...

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Vydáno v:PeerJ. Computer science Ročník 10; s. e1996
Hlavní autor: Alsubai, Shtwai
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States PeerJ. Ltd 19.04.2024
PeerJ Inc
Témata:
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
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Shrnutí: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.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.1996