Abstract
Infectious keratitis is the leading cause of blindness in the word where bacteria keratitis (BK) and fungi keratitis (FK) are common causes of infection. As an ophthalmic emergency, BK and FK need to be treated correctly as soon as possible to prevent irrecoverable damage to vision, but the early correct diagnosis between them is challenging. Some research have shown that even trained ophthalmologists have less than 80% accuracy in correctly diagnosing FK from BK. In this paper, a Fine-Grained model called BF-Net is proposed to improve the accuracy of automatic diagnosis of keratitis, with consideration of the characteristics of keratitis images. We build a keratitis dataset containing 1433 Slit-Lamp images of BK or FK from 458 patients and conducted detailed experiments to prove the effectiveness of our method. The Precision, Recall, Accuracy, AUC and F1 score of our method are 82.34%, 87.20%, 83.12%, 0.85 and 0.85 respectively, which has achieved the best effect compared with other classification methods. Furthermore, visualization technique Grad-CAM++ is used to provide interpretability for the validity of our model.
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Lin, K., Zhang, J., Jiang, X., Liu, J., Zhou, S. (2023). BF-Net: A Fine-Grained Network for Identify Bacterial and Fungal Keratitis. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. https://doi.org/10.1007/978-3-031-44216-2_5
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