ArticleJournal of fungi (Basel, Switzerland)2021
Multi-Scale Convolutional Neural Network for Accurate Corneal Segmentation in Early Detection of Fungal Keratitis.
Article in Journal of fungi (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 29 citations in OpenAlex.
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Self-knowledge distillation-empowered directional connectivity transformer for microbial keratitis biomarkers segmentation on slit-lamp photography.Medical image analysis · 2025Article
- Causative Agents of Fungal Keratitis in Northeastern Iran: A 3-Year Tertiary Care Hospital Study.The American journal of tropical medicine and hygiene · 2025Article
- A Convolutional Neural Network Using Anterior Segment Photos for Infectious Keratitis Identification.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- Bacterial and Fungal Keratitis in a Tertiary Care Hospital from Romania.Microorganisms · 2024Article
- Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks.Translational vision science & technology · 2023Article
- Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids.Diagnostics (Basel, Switzerland) · 2023Article
- A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification.Scientific reports · 2023Article
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
- Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Deep Learning Approach in Image Diagnosis ofDiagnostics (Basel, Switzerland) · 2022Article
- Ocular Fungal Infections.Journal of fungi (Basel, Switzerland) · 2022Article
- Comparisons of deep learning algorithms for diagnosing bacterial keratitis via external eye photographs.Scientific reports · 2021Article
- Multimodal Deep Learning for Differentiating Bacterial and Fungal Keratitis Using Prospective Representative Data.Ophthalmology scienceArticle
- Applications of Computer Vision for Infectious Keratitis: A Systematic Review.Ophthalmology scienceReview
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microbial keratitis is an infection of the cornea of the eye that is commonly caused by prolonged contact lens wear, corneal trauma, pre-existing systemic disorders and other ocular surface disorders. It can result in severe visual impairment if improperly managed. According to the latest World Vision Report, at least 4.2 million people worldwide suffer from corneal opacities caused by infectious agents such as fungi, bacteria, protozoa and viruses. In patients with fungal keratitis (FK), often overt symptoms are not evident, until an advanced stage. Furthermore, it has been reported that clear discrimination between bacterial keratitis and FK is a challenging process even for trained corneal experts and is often misdiagnosed in more than 30% of the cases. However, if diagnosed early, vision impairment can be prevented through early cost-effective interventions. In this work, we propose a multi-scale convolutional neural network (MS-CNN) for accurate segmentation of the corneal region to enable early FK diagnosis. The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. The model trained on the segmented images in the region of interest, achieved a diagnostic accuracy of 88.96%. The features learnt by the model emphasize that it can correctly identify dominant corneal lesions for detecting FK.
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Registered trials
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