Lymph node sections detection based on deep convolutional neural networks
Yuchen Song, Xuejian Zhang · Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering · 2021
Due to the severe damage to the health of a human being caused by breast cancer, it is rather crucial to explore novel approaches to detect images of lymph node sections to replace traditional methods, which are both time-consuming and inaccurate. This paper proposed an efficient cancer detection method by applying various kinds of convolutional neural networks (CNN) with a user interface. To achieve the best accuracy, experiments and comparisons are made between VGG16, ResNet-18, and EfficientNet, which are three popular models in the field of image processing. We also made a comparison to emphasize the importance of pre-trained weights of VGG16. Our models achieved high accuracy with pre-trained weights. In particular, VGG16 achieved 0.8340 in accuracy and 0.8326 in recall on the PatchCamelyon dataset and was used in our application. Our source code is available at https://github.com/bqdqj/Cancer-detection-based-on-tensorflow-and-PyQt5.