Metastatic Cancer Image Classification Based On Deep Learning Method

TL;DR

This paper introduces a novel deep learning approach combining DenseNet169 and RAdam for improved classification of metastatic cancer in histopathological images, outperforming classical CNNs.

Contribution

The study proposes a new method integrating DenseNet169 with RAdam optimizer for better accuracy in metastatic cancer image classification.

Findings

DenseNet169 achieved 1.77% higher AUC-Roc than Vgg19.

Accuracy was 1.50% higher with DenseNet169.

Analyzed loss and batch relationship during training and validation.

Abstract

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.