Enhanced Lung Cancer Detection and TNM Staging Using YOLOv8 and TNMClassifier: An Integrated Deep Learning Approach for CT Imaging

This paper introduces an advanced method for lung cancer subtype classification and detection using the latest version of YOLO, tailored for the analysis of CT images. Given the increasing mortality rates associated with lung cancer, early and accurate diagnosis is crucial for effective treatment planning. The proposed method employs single-shot object detection to precisely identify and classify various types of lung cancer, including Squamous Cell Carcinoma (SCC), Adenocarcinoma (ADC), and Small Cell Carcinoma (SCLC). A publicly available dataset was utilized to evaluate the performance of YOLOv8. Experimental outcomes underscore the system’s effectiveness, achieving an impressive mean Average Precision (mAP) of 97.1%. The system demonstrates the capability to accurately identify and categorize diverse lung cancer subtypes with a high degree of accuracy. For instance, the YOLOv8 Small model outperforms others with a precision of 96.1% and a detection speed of 0.22 seconds, surpassing other object detection models based on two-stage detection approaches. Building on these results, we further developed a comprehensive TNM classification system. Features extracted from the YOLO backbone were reduced using Principal Component Analysis (PCA) to enhance computational efficiency. These reduced features were then fed into a custom TNMClassifier, a neural network designed to classify the Tumor, Node, and Metastasis (TNM) stages. The TNMClassifier architecture comprises fully connected layers and dropout layers to prevent overfitting, achieving an accuracy of 98% in classifying the TNM stages. Additionally, we tested the YOLOv8 Small model on another dataset, the Lung3 dataset from the Cancer Imaging Archive (TCIA). This testing yielded a recall of 0.91, further validating the model’s effectiveness in accurately identifying lung cancer cases. The integrated system of YOLO for subtype detection and the TNMClassifier for stage classification shows significant potential to assist healthcare professionals in expediting and refining diagnoses, thereby contributing to improved patient health outcomes.