FOCUSNET-LC: A DEEP LEARNING FRAMEWORK AND ALGORITHM FOR EFFICIENT AND EXPLAINABLE LUNG CANCER DETECTION

For lung cancer, one of the leading global causes of cancer death, accurate and early detection is essential to improve treatment success. Current practices for classification response prediction, such as DL-LCD and MFDNN, achieve high accuracy but are limited by the fact that they (i) lack interpretability, (ii) require multimodal data, and (ii) are inefficient in concentrating on clinically relevant areas. These limitations prevent their implementation in real clinical contexts and highlight the need for innovative and reliable interpretative solutions. This study introduces the FocusNet-LC model and FocusNet-LC Based Lung Cancer Detection Algorithm, which is built to deal with these difficulties. The FocusNet-LC model combines region of interest (ROI) segmentation, metadata incorporation, and model explainability (Grad-CAM) to deliver accurate, interpretable, and clinically relevant predictions. The corresponding algorithm utilizes the features from the model to efficiently process data, extract features, and classify them. The proposed framework achieves 97.36% accuracy on the IQ-OTH/NCCD dataset and produces higher precision, recall, and F1-score compared to state-of-the-art models. Thus, the usefulness of this framework comes from providing explanatory and correct predictions with calculation speed. This study also represents a significant advancement in early lung cancer diagnostics and improved patient management by not just filling in existing methodological gaps.