Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis

TL;DR

This paper introduces a deep learning model for classifying infectious keratitis from clinical images, significantly outperforming ophthalmologists in diagnostic accuracy.

Contribution

The study presents a novel sequential deep learning approach that preserves spatial image structures and improves classification accuracy for infectious keratitis.

Findings

Deep model achieved 80% accuracy, outperforming ophthalmologists' 49.27%.

Proposed method effectively captures subtle features in clinical images.

Model demonstrates potential for rapid, accurate diagnosis in clinical settings.

Abstract

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.