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I S S N 2319-2690
IJRSAT
International Journal for Research In Science & Advanced Technologies
" Enriching The Research "
International, Peer Reviewed, Open Access Journal
ISSN Approved Journal No. 2319-2690
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DOI Prefix: 10.65726
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Explainable AI in Lung Cancer Detection: U-Net and CNN Ensemble Approach
Dr. Abdul khadeer, Asra Sana
DOI: Not available
Year: 2026  |  Volume: 26  |  Issue: 8
Date of Publication: 2026/08/05
Keywords: Explainable AI (XAI), LLM, Nodule Detection, Benign, Malignant, Deep learning, Ensemble.

Abstract

Lung cancer stands as the most prevalent form of cancer while maintaining its position as the main reason for cancer deaths worldwide. Detecting lung cancer early and precisely plays an essential role in achieving better clinical results. Medical imaging has reached remarkable success because of deep learning applications whose main focus is lung nodule detection along with classification tasks. The current research extends existing ensemble Convolutional Neural Networks (CNNs) detection methods for lung cancer by adopting modern segmentation protocols and classification frameworks and explainable features. The LUNA16 dataset trained U-Net model performs segmentation of lung nodules in CT scan images before the classification process focuses exclusively on relevant regions. Segmented nodules undergo classification either as malignant or benign through the application of ResNet101, MobileNetV3-small, and a custom CNN classifier, all pre-trained on the LIDC-IDRI dataset. Performance comparisons are made between the ensemble approach and each individual model when their predictions are combined through a soft ensemble framework. Additionally, SHAP (Shapley Additive Explanations) provides important feature explanations that influence classification. The integration of a Large Language Model (LLM) happens subsequent to explainability for the generation of contextual information which aids both clinicians' decision-making and improves interpretation outcomes. The ensemble system achieves superior performance results when compared to standalone networks and its combination with explainable AI and LLM-based interpretation generates a transparent diagnostic tool for lung cancers.

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