Lung cancer remains as one such leading cause of cancer related mortality around the world, demanding precise and early detection through intelligent and effective diagnostic systems. This model presents a Modified Shark Smell Optimization (MSSO) aided Deep Transfer Learning framework for explainable lung cancer detection using Computed Tomography (CT) images. The workflow begins with preprocessing input image carried by segmentation using DeepLabV3 + model to, precisely isolate lung regions and thus eradicate background noise. The features segmented are further processed using Enriched DNN model for enhancing the robustness of feature and to reduce redundancy. Subsequently, the MSSO approach is employed for attaining most relevant and discriminative feature subset for classification. An optimized feature subset s then classified with the use of Deep Transfer Learning-based Inception ResNetV2 model, which enhances recognition accuracy and ensures fast convergence. For enhancing transparency of model and clinical trust, Explainable AI (XAI) is incorporated using GradCAM + + for visualizing critical lung regions thus contributing to decision making process. An experimental outcome on LIDC-IDRI dataset demonstrates that proposed hybrid MSSO-InceptionResNetV2 scheme attains superior accuracy, sensitivity, specificity, ROC, and precision with reduced FPR and computational time on comparing traditional models considered. Hence, the proposed scheme offers reliable and interpretable solutions for automated diagnosis of lung cancer, offering significant potential for clinical integration, early intervention, and enhanced patient prognosis.
Prabha et al. (Mon,) studied this question.
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