Metastatic Cancer Image Classification Based On Deep Learning Method
This work addresses the challenge of accurate cancer detection in medical imaging, but it is incremental as it applies existing deep learning components to a specific domain.
The paper tackled the problem of automatically classifying metastatic cancer from histopathological images by proposing a method combining DenseNet169 and Rectified Adam optimization, achieving a 1.77% higher AUC-ROC score and 1.50% higher accuracy compared to Vgg19.
Using histopathological images to automatically classify cancer is a difficult task for accurately detecting cancer, especially to identify metastatic cancer in small image patches obtained from larger digital pathology scans. Computer diagnosis technology has attracted wide attention from researchers. In this paper, we propose a noval method which combines the deep learning algorithm in image classification, the DenseNet169 framework and Rectified Adam optimization algorithm. The connectivity pattern of DenseNet is direct connections from any layer to all consecutive layers, which can effectively improve the information flow between different layers. With the fact that RAdam is not easy to fall into a local optimal solution, and it can converge quickly in model training. The experimental results shows that our model achieves superior performance over the other classical convolutional neural networks approaches, such as Vgg19, Resnet34, Resnet50. In particular, the Auc-Roc score of our DenseNet169 model is 1.77% higher than Vgg19 model, and the Accuracy score is 1.50% higher. Moreover, we also study the relationship between loss value and batches processed during the training stage and validation stage, and obtain some important and interesting findings.