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Infectious keratitis is the most common entities of corneal diseases, inwhich pathogen grows in the cornea leading to inflammation and destruction ofthe corneal tissues. Infectious keratitis is a medical emergency, for which arapid and accurate diagnosis is needed for speedy initiation of prompt andprecise treatment to halt the disease progress and to limit the extent ofcorneal damage; otherwise it may develop sight-threatening and eveneye-globe-threatening condition. In this paper, we propose a sequential-leveldeep learning model to effectively discriminate the distinction and subtlety ofinfectious corneal disease via the classification of clinical images. In thisapproach, we devise an appropriate mechanism to preserve the spatial structuresof clinical images and disentangle the informative features for clinical imageclassification of infectious keratitis. In competition with 421ophthalmologists, the performance of the proposed sequential-level deep modelachieved 80.00% diagnostic accuracy, far better than the 49.27% diagnosticaccuracy achieved by ophthalmologists over 120 test images.

Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis