ArticleJournal of healthcare engineering2017
Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Research status and progress of deep learning in automatic esophageal cancer detection.World journal of gastrointestinal oncology · 2025Review
- Development of a Deep Learning System to Detect Esophageal Cancer by Barium Esophagram.Frontiers in oncology · 2022Article
- Detection of Collaterals from Cone-Beam CT Images in Stroke.Sensors (Basel, Switzerland) · 2021Article
- COVID-19 discrimination framework for X-ray images by considering radiomics, selective information, feature ranking, and a novel hybrid classifier.Signal processing. Image communication · 2021Article
- Auto-detection of cervical collagen and elastin in Mueller matrix polarimetry microscopic images using K-NN and semantic segmentation classification.Biomedical optics express · 2021Article
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Authors and funding
7 authors.
Funding
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Abstract
Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and
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