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Computer-Aided Diagnosis of Laryngeal Cancer Via Deep Learning Based on Laryngoscopic Images

التفاصيل البيبلوغرافية
العنوان: Computer-Aided Diagnosis of Laryngeal Cancer Via Deep Learning Based on Laryngoscopic Images
المؤلفون: Jin-Gang Yu, Yuewen Lu, Jin Ye, Jingjing Xu, Zebin Jiang, Yuanqing Li, Haidi Yang, Hao Xiong, Mingyue Liu, Yuan Tao, Wenjie Hu, Peiliang Lin, Lichao Xiao, Wei Lin, Yiqing Zheng, Huaifeng Liu
المصدر: EBioMedicine
EBioMedicine, Vol 48, Iss, Pp 92-99 (2019)
بيانات النشر: Elsevier BV, 2019.
سنة النشر: 2019
مصطلحات موضوعية: 0301 basic medicine, medicine.medical_specialty, Research paper, Normal tissue, lcsh:Medicine, Diagnostic system, Convolutional neural network, Sensitivity and Specificity, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, Deep Learning, Image Processing, Computer-Assisted, medicine, Humans, Medical physics, Diagnosis, Computer-Assisted, Stage (cooking), Head and neck, Laryngeal Neoplasms, lcsh:R5-920, Training set, Laryngoscopy, business.industry, Deep learning, lcsh:R, Reproducibility of Results, Cancer, Ethical review, General Medicine, Models, Theoretical, medicine.disease, 030104 developmental biology, Computer-aided diagnosis, 030220 oncology & carcinogenesis, Radiology, Artificial intelligence, lcsh:Medicine (General), business
الوصف: Background: Laryngeal cancer (LCA) is a common malignancy of the head and neck region. Early diagnosis of LCA is very difficult because of its subtle abnormalities in early stage, especially for the inexperienced endoscopists with conventional white-light endoscopy. Computer-aided diagnosis were used for several diseases in recent years. This study was to develop a deep convolutional neural network (DCNN) that can automatically detect LCA in laryngoscopic images. Methods: A DCNN-based diagnostic system was constructed and trained using 15,239 laryngoscopic images of LCA, precancerous laryngeal lesions (PRELCA), benign laryngeal tumors (BLT) and normal tissues (NORM). An independent test set of 1,200 laryngoscopic images was applied to the constructed DCNN to evaluate its performance against experienced endoscopists. Findings: In the training set, the DCCN achieved the sensitivity of 0.920, the specificity of 0.716, the AUC of 0.922, and the overall accuracy of 0.858 for detecting LCA and PRELCA among all lesions and normal tissues. When compares to human experts in an independent test set, the DCCN' s performance on detection of LCA and PRELCA achieved the sensitivity of 0.933, the specificity of 0.797, the AUC of 0.952, and the overall accuracy of 0.901, which is comparable to that of experienced human expert with 10-20 years of work experience. Moreover, the overall accuracy of DCNN for detection of LCA is 0.786, which is also comparable to that of experienced human expert with 10-20 years of work experience and exceed the experts with less than 10 years of work experience. Interpretation: The DCNN had high sensitivity and specificity for automated detection of LCA and PRELCA from BLT and NORM in laryngoscopic images. This novel and effective approach facilitates earlier diagnosis of early LCA, resulting in improved clinical outcomes and reducing the burden of endoscopists. Funding: None. Declaration of Interest: The authors have no conflict of interests. Ethical Approval: The study was approved by the ethical review board of Sun Yat-sen Memorial Hospital, Sun Yat-sen University.
تدمد: 1556-5068
DOI: 10.2139/ssrn.3391172
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55c6dc513d09b641fbdaeb8af0ead886
https://doi.org/10.2139/ssrn.3391172
Rights: OPEN
رقم الانضمام: edsair.doi.dedup.....55c6dc513d09b641fbdaeb8af0ead886
قاعدة البيانات: OpenAIRE
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