Publication

Abstract : In this paper, the local and global feature importance for lung cancer diagnosis is explained using SHAP (Shapley Additive explanations) and LIME (Local Interpretable Model-agnostic Explanations). Firstly, the images from the IQ-OTH/NCCD Lung Cancer Dataset for three classes - normal, malignant, benign of the dataset are pre-processed and feature extraction is done using various CNN (Convolution Neural Network) architectures, namely - VGG16, ResNet - 50, InceptionV3, MobileNetV2, EfficientNet - B0. The extracted features are used to train models based on various Machine Learning (ML) classifiers. From the outcome of the classifications, it is observed that SVM with VGG-16 based feature extraction offers superior performance with precision, recall and F1 score of 97%, with an average accuracy of 98%. Further, explainable AI frameworks (SHAP, LIME) are used to produce insights into the model's predictions.