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Skin Lesion Classification Based on Various Machine Learning Models Explained by Explainable Artificial Intelligence

Gupta, Sarthak (2023) Skin Lesion Classification Based on Various Machine Learning Models Explained by Explainable Artificial Intelligence. Masters thesis, Dublin, National College of Ireland.

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Abstract

Over the past few years, many lives have been lost due to the spread of various deadly diseases. There has also been a big rise in deaths from skin cancer, and most of these people could have lived longer if the cancer had been detected at an early stage. Therefore, a skin lesion classification model is proposed in this project, which will help in classification of skin lesions. A major problem with using artificial intelligence in the medical sector is a lack of transparency. Therefore, Explainable Artificial Intelligence (XAI) is used to explain the proposed classification model built using deep learning and machine learning models such as Convolutional Neural Networks (CNN) and Extreme Gradient Boosting (XGB Classifier). Two explainable artificial methods, such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive Explanations (SHAP), are used to explain the proposed models. The Convolutional Neural Networks (CNN) model performed on the augmented images produced an accuracy of 75.94%, which outperformed the other two models performed in this research. The explainable artificial intelligence method (SHAP) was used to explain the attribute importance, and the Local Interpretable Model-Agnostic Explanation (LIME) was used to explain how the model interprets images to predict its class.

Item Type: Thesis (Masters)
Supervisors:
Name
Email
Anant, Aaloka
UNSPECIFIED
Uncontrolled Keywords: Explainable Artificial Intelligence (XAI); Local Interpretable Model-Agnostic Explanations (LIME); SHapley Additive Explanations (SHAP)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 18 May 2023 14:58
Last Modified: 18 May 2023 14:58
URI: https://norma.ncirl.ie/id/eprint/6591

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