Automatic Lymph Node Classification with Convolutional Neural Network
Issued Date
2022-01-01
Resource Type
Scopus ID
2-s2.0-85143639805
Journal Title
ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering
Start Page
223
End Page
228
Rights Holder(s)
SCOPUS
Bibliographic Citation
ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering (2022) , 223-228
Suggested Citation
Uthatham A., Yodrabum N., Sinmaroeng C., Titijaroonroj T. Automatic Lymph Node Classification with Convolutional Neural Network. ICITEE 2022 - Proceedings of the 14th International Conference on Information Technology and Electrical Engineering (2022) , 223-228. 228. doi:10.1109/ICITEE56407.2022.9954045 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84328
Title
Automatic Lymph Node Classification with Convolutional Neural Network
Author(s)
Other Contributor(s)
Abstract
Manual lymph node classification is a tedious and time-consuming task. It requires a histopathologist to discriminate a lymph node from other look-alike kinds of tissues. The lymph node is easily misunderstood with other tissues because its shape and color might be similar to the others tissue around it. To automate this task, we present an automatic lymph node classification with convolutional neural network (CNN). In addition, we compared eight existing CNNs to ensure that we discover the best architecture for discriminating lymph node. DenseNet architecture provided the highest performance among AlexNet, VGG, GoogLeNet, ResNet, SqueezeNet, MobileNet, and EfficientNet, the highest accuracy at 0.994 and an F1score of 0.996. DenseNet accomplished the highest performance from two advantages: (i) fewer parameters and (ii) Dense connectivity.