Explainable AI for Early Lung Cancer Detection: A Path to Confidence

Abstract

One of the most deadly types of cancer is lung cancer, and effective treatment depends on early detection. To detect lung cancer in its early stages, numerous computer-aided diagnostic (CAD) methods have been developed. Due to their excellent accuracy, these methods mostly use machine learning models. The medical profession continues to be cautious despite their accuracy because there aren't any obvious justifications for their predictions. In order to provide comprehensible explanations for identifying lung cancer in chest X-ray pictures using convolutional neural network-based models, we propose the use of Explainable AI (XAI) approaches. Our main goal is to boost the medical community's confidence in the accuracy of machine learning methods for making diagnoses of illnesses.

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