Lung cancer remains a major cause of cancer-related deaths worldwide, highlighting the need for early and precise detection. This project utilizes YOLOv11, an advanced object detection model, to automatically identify and classify lung nodules in CT scans. The model accurately categorizes nodules into three types: Normal, Benign, and Malignant. Additionally, a TNM classification system is incorporated to assess tumor staging, enhancing the diagnostic process. By leveraging state -of-the-art detection and classification techniques, this approach aims to improve diagnostic accuracy and facilitate early intervention. Performance is evaluated using precision, recall, F1-score, and mAP.
Lung cancer classification, computed tomography, convolutional neural network, CNN–SVM, attention mechanism, computer-aided diagnosis.
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