Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis
| Autor: | Qiang Zhu, Wen-jia Xie, Runping Duan, Siliang Tang, Yu-Feng Yao, Zhengqing Fang, Fei Wu, Ming Kong, Ye-sheng Xu, Yuxiao Lin |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
medicine.medical_specialty Environmental Engineering genetic structures General Computer Science Computer Vision and Pattern Recognition (cs.CV) Materials Science (miscellaneous) General Chemical Engineering Computer Science - Computer Vision and Pattern Recognition Energy Engineering and Power Technology 02 engineering and technology Infectious Keratitis 010402 general chemistry 01 natural sciences Corneal disease Ophthalmology Cornea Machine learning Long short-term memory FOS: Electrical engineering electronic engineering information engineering medicine Contextual image classification Corneal Damage business.industry Corneal Diseases Image and Video Processing (eess.IV) Disease progression General Engineering Deep learning Electrical Engineering and Systems Science - Image and Video Processing Engineering (General). Civil engineering (General) 021001 nanoscience & nanotechnology eye diseases 0104 chemical sciences medicine.anatomical_structure sense organs TA1-2040 0210 nano-technology business Sequential features Feature learning |
| Zdroj: | Engineering, Vol 7, Iss 7, Pp 1002-1010 (2021) |
| ISSN: | 2095-8099 |
| Popis: | Infectious keratitis is the most common entities of corneal diseases, in which pathogen grows in the cornea leading to inflammation and destruction of the corneal tissues. Infectious keratitis is a medical emergency, for which a rapid and accurate diagnosis is needed for speedy initiation of prompt and precise treatment to halt the disease progress and to limit the extent of corneal damage; otherwise it may develop sight-threatening and even eye-globe-threatening condition. In this paper, we propose a sequential-level deep learning model to effectively discriminate the distinction and subtlety of infectious corneal disease via the classification of clinical images. In this approach, we devise an appropriate mechanism to preserve the spatial structures of clinical images and disentangle the informative features for clinical image classification of infectious keratitis. In competition with 421 ophthalmologists, the performance of the proposed sequential-level deep model achieved 80.00% diagnostic accuracy, far better than the 49.27% diagnostic accuracy achieved by ophthalmologists over 120 test images. Comment: Accepted by Engineering |
| Databáze: | OpenAIRE |
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