Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis

Abstract Infectious keratitis is the most common condition of corneal diseases in which a 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 to ensure prompt and precise treatment to halt the disease progression and to limit the extent of corneal damage; otherwise, it may develop a sight-threatening and even eye-globe-threatening condition. In this paper, we propose a sequential-level deep learning model to effectively discriminate 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 a comparison, the performance of the proposed sequential-level deep model achieved 80.00% diagnostic accuracy, far better than the (49.27 ± 11.5)% diagnostic accuracy achieved by 421 ophthalmologists over 120 test images.