Intelligent Squirrel Search-Optimized VGG16 with HOG Feature Fusion for High-Accuracy Lung Cancer Classification from DICOM Images

Accurate classification and early detection of lung cancer are crucial for effective treatment and improved patient outcomes. Although recent advances in medical imaging have improved diagnostic workflows, many traditional approaches remain limited in their ability to efficiently analyze large volumes of imaging data. Accordingly, this study proposes a deep learning framework for lung cancer classification using Digital Imaging and Communications in Medicine (DICOM) images. The proposed approach integrates an Intelligent Squirrel Search (ISS)–tuned VGG16 network (ISS-VGG16) to improve classification performance. A set of lung cancer DICOM images was obtained from an open-source dataset. Preprocessing steps, including image resizing and contrast enhancement, were applied to standardize the inputs for model training. ISS was used to optimize key hyperparameters of the VGG16 model, thereby improving classification performance. The model was implemented in Python. Experimental results indicate that the proposed ISS-VGG16 approach outperforms baseline methods, achieving a recall of 0.92, precision of 0.96, F1-score of 0.95, and overall accuracy of 0.97 on the evaluated lung cancer image dataset. These results demonstrate reliable classification performance across the defined lung cancer classes. Overall, the proposed framework provides an effective and dependable approach for lung cancer image classification.