Grad-Cam Empowered Lung Nodule Detecting Using Resnet50 - Shaini, Ushasree; Boda, Nithin; Lingampally, Abhiram; Suneetha, M; Jamal, K (Journal of Neonatal Surgery, 2025)
Lung cancer is a severe worldwide health issue that raises mortality rates. Improving patient survival depends on early detection. Using Grad-CAM and ResNet50, a deep convolutional neural network (CNN), this study offers an original approach for lung nodule detection. The suggested approach uses the sophisticated feature extraction capabilities of ResNet50 to recognize and categorize nodules in computed tomography (CT) data. Grad-CAM is used to improve the model's interpretability by providing visual explanations of its predictions. This helps healthcare professionals validate the results and boosts system trust. To guarantee strong generalization and enhance model performance, the dataset received extensive preprocessing, which included normalization and augmentation. The technique's remarkable accuracy of 0.98 shows the precision with which it can identify lung nodules. This illustrates how ResNet50 and Grad-CAM can be combined to improve medical diagnoses by increasing the precision and understanding of AI algorithms. Professional annotations validated the model's precision in recognizing patterns in intricate medical imagery. In addition to demonstrating the viability of autonomous lung nodule identification, this work stresses the significance of model transparency, which promotes user trust in clinical contexts. In order to provide even more thorough visual explanations, future research will concentrate on developing the model by adding new datasets and putting Grad-CAM++ into practice. All things considered, this study has encouraging implications for enhancing lung cancer recognition and diagnosis and constitutes a substantial advancement in the use of AI-driven, interpretable medical diagnostics in clinical practice.