Feed

The identification of metastatic tissues in histopathological scans is a critical step in cancer detection. This research utilizes DenseNet-121 to automate the detection of metastatic tissue using the CAMELYON17 dataset, along with some data augmentation techniques to improve model generalization. The results show that DenseNet-121 with data augmentation outperforms ResNet-18 and ResNet-50 in terms of accuracy and F1-score in detecting metastatic tissue, achieving impressive results with a testing accuracy of 0.98 and an F1-score of 0.98, which is higher than previous state-of-the-art methods. Moreover, the model's performance was tested on the CAMELYON16 dataset, and it still maintained relatively high accuracy on previously unseen images. These results suggest that DenseNet-121 could be a valuable assistive tool to pathologists, potentially accelerating cancer diagnoses and improving diagnostic reliability.