
Application of Deep Transfer Learning to the Classification of Colorectal Cancer Lymph Node Metastasis
Accurate classifications of colorectal cancer (CRC) lymph
node metastasis (LNM) could assist radiologists in increasing
the diagnostic accuracy and help surgeons establish a correct
surgical plan. This study aims to present an efficient pipeline with
deep transfer learning for CRC LNM classification. Hence, 11
deep pre-trained models have been investigated on a CRC LN
dataset. The dataset of this experiment is from Harbin Medical
University Cancer Hospital. This dataset contains samples of 619
patients. Among these samples, 312 were positive and 307 were
negative. In addition, datasets with different dimensions and various
training epochs were also studied to ascertain the minimum training
dataset and training times. In order to improve the interpretability
of the model classification performance, a visual convolution layer
feature map was first established to compute the similarity distance
between the feature map and original data. The experimental results
revealed that resnet_152 was the best deep pre-trained model for
the classification of CRC LNM, with an accuracy of 97.2%, with 600
raw data samples being the minimum dimension of a dataset and 30
epochs the minimum training times in the CRC LNM classification.
This study suggests that the proposed deep transfer learning
pipeline could classify the CRC LNM with high efficiency, without
requiring sophisticated computational knowledge for radiologists.
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Affiliations: 1: Automation College, Harbin Engineering University, Harbin 150001, Heilongjiang Province, China 2: Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150001, Heilongjiang Province, China
Appeared or available online: November 14, 2020