Ensemble Machine Learning Approaches for Automated Fungal Keratitis Diagnosis Using In Vivo Confocal Microscopy
Sowmya Kamath S1, Shikha Reji1, Vaibhava Lakshmi1
1Healthcare Analytics and Language Engineering (HALE) Lab Department of Information Technology National Institute of Technology, Surathkal Mangaluru Karnataka India.
Abstract:
Fungal keratitis (FK) is a severe ocular infection that can lead to significant vision problems or blindness if not diagnosed and treated promptly. Early and accurate detection of FK is essential for effective management. Traditional diagnostic methods are often time-consuming and require specialized laboratory resources. Recently, advances in artificial intelligence and computer vision have enabled automated diagnosis of FK using slit-lamp images. In this article, a comprehensive evaluation of state-of-the-art techniques adopted for classifying FK using in vivo confocal microscopy (IVCM) images is presented. Detailed experiments and performance evaluation of various machine learning models are systematically performed, with a focus on evaluating the effect of diverse techniques for image processing, data augmentation, hyperparameters and model finetuning to assess each model's strengths and limitations. Experiments revealed that applying green channel preprocessing with a 12-feature set achieved 99% accuracy using Random Forest, highlighting its effectiveness in FK detection, while complex techniques like histogram modelling reduced accuracy to 64%. Robust models like AdaBoost and RUSBoost maintained high F1-scores, demonstrating adaptability to imbalanced medical datasets, and to real-world clinical scenarios.
Insights
Accurate fungal keratitis (FK) detection is crucial for preventing vision loss. Machine learning models analyzing in vivo confocal microscopy (IVCM) images achieved 99% accuracy, offering a promising diagnostic tool.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Fungal keratitis (FK) is a severe eye infection risking vision loss.
- Prompt diagnosis and treatment are vital for managing FK.
- Current diagnostic methods can be slow and resource-intensive.
Purpose of the Study:
- To evaluate state-of-the-art machine learning techniques for classifying FK using in vivo confocal microscopy (IVCM) images.
- To assess the impact of various image processing and model tuning strategies on FK detection accuracy.
- To identify robust models adaptable to clinical settings and imbalanced datasets.
Main Methods:
- Systematic evaluation of machine learning models for FK classification from IVCM images.
- Experimentation with diverse image processing techniques, data augmentation, and hyperparameter tuning.
- Performance assessment focusing on accuracy, F1-scores, and model adaptability.
Main Results:
- A Random Forest model with green channel preprocessing and a 12-feature set achieved 99% accuracy in FK detection.
- Complex methods like histogram modeling yielded lower accuracy (64%).
- AdaBoost and RUSBoost models demonstrated robustness and high F1-scores, suitable for imbalanced datasets.
Conclusions:
- Machine learning analysis of IVCM images offers a highly accurate method for fungal keratitis detection.
- Specific preprocessing techniques (green channel) and feature sets significantly enhance diagnostic performance.
- Robust models like AdaBoost and RUSBoost show promise for real-world clinical application in FK diagnosis.
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