Abstract
BACKGROUND This study used CT imaging analyzed with deep learning techniques to assess the diagnostic accuracy of lung metastasis detection in patients with breast cancer. The aim of the research was to create and verify a system for detecting malignant and metastatic lung lesions that uses YOLOv10 and transfer learning. MATERIAL AND METHODS From January 2023 to 2024, CT scans of 16 patients with breast cancer who had confirmed lung metastases were gathered retrospectively from Erzincan Mengücek Gazi Training and Research Hospital. The YOLOv10 deep learning system was used to assess a labeled dataset of 1264 enhanced CT images. RESULTS A total of 1264 labeled images from 16 patients were included. With an accuracy of 96.4%, sensitivity of 94.1%, specificity of 97.1%, and precision of 90.3%, the ResNet-50 model performed best. The robustness of the model was shown by the remarkable area under the curve (AUC), which came in at 0.96. After dataset tuning, the GoogLeNet model's accuracy was 97.3%. These results highlight our approach's improved diagnostic capabilities over current approaches. CONCLUSIONS This study shows how YOLOv10 and transfer learning can be used to improve the diagnostic precision of pulmonary metastases in patients with breast cancer. The model's effectiveness is demonstrated by the excellent performance metrics attained, opening the door for its application in clinical situations. The suggested approach supports prompt and efficient treatment decisions by lowering radiologists; workload and improving the early diagnosis of metastatic lesions.