Abstract
Accurate clinical cancer T-stage diagnosis is crucial for effective treatment. However, it is difficult, time-consuming, and laborious for physicians to recognize T-stage manually using rectal Magnetic Resonance Imaging (MRI) images. Machine learning methods have played important roles in medical image processing. With the goal of automatic rectal cancer T-stage prediction, we train the proposed Feature Extraction based Support Vector Machine (FE-SVM) model with the newly acquired dataset consisting of 147 patients’ MRI images with primary rectal cancer. Our method adapts SVM as the training framework as SVM is effective enough for dealing with small datasets as opposed to state-of-the-art deep learning models. FE-SVM firstly extracts image similarity as an initial feature because the feature of image similarity can better reflect the differences among various types of MRI images, and the final 10-dimensional features are obtained by a 5-layers Autoencoder. To evaluate the performance of FE-SVM, we compared it with six benchmark models: CNN, Alexnet, Resnet18, Resnet50, Capsule Network, and Random Forest. FE-SVM outperforms these state-of-the-art models with significant evaluation scores.
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Acknowledgment
Prof. Wei Pang (School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK) is participated in writing or technical editing of the manuscript. This research is supported by the National Natural Science Foundation of China (Grants Nos.61772227, 61972174, 61972175), Science and Technology Development Foundation of Jilin Province (No. 20180201045GX, 20200201300JC, 20200401083GX,20200201163JC), the Jilin Development and Reform Commission Fund(No. 2020C020-2).
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Our research is approved by the ethics committee of Jilin university. The authors declare no conflict of interest.
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Yizhang Wang and Tingting Gong contribute equally to the article.
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Wang, Y., Gong, T., Hassan, M. et al. A feature extraction based support vector machine model for rectal cancer T-stage prediction using MRI images. Multimed Tools Appl 80, 30907–30917 (2021). https://doi.org/10.1007/s11042-021-11165-8
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DOI: https://doi.org/10.1007/s11042-021-11165-8