| dc.description.abstract | This study proposes an automated classification system combining geometric fast data density functional transformation (g-fDDFT) with deep learning for breast cancer sentinel lymph node tissue image classification. Breast cancer is considered one of the leading causes of death among women, and its prognosis is highly correlated with sentinel lymph node metastasis status, making early diagnosis crucial for improving survival rates. In breast cancer diagnosis, histopathological images are typically used as the gold standard for diagnosis. However, the classification of breast cancer histopathological images needs to be performed by pathologists, making this meticulous examination process time-consuming and potentially missing smaller metastatic cells. The CAMELYON17 pathology image open database was used for data. Methodologically, g-fDDFT was first used to extract features, followed by the level set method to segment cell nuclei, and then deep learning models were used to classify metastatic and non-metastatic images. In terms of results, the system successfully reduced processing time from 12,720 seconds to 2,059 seconds, improving efficiency by 83.8%. Regarding classification performance, F1 score reached 75%, a 15% improvement over traditional methods, though accuracy decreased by 13%, indicating areas for future improvement. This study demonstrates that g-fDDFT can effectively process large pathological images, providing a new solution for pathological diagnosis automation. While there is still room for improvement in classification accuracy, the system′s demonstrated efficiency advantages pioneer new directions for the future development of medical image analysis. | en_US |