Deep Learning-Based Pipeline for Acanthamoeba Keratitis Cyst Detection : Image Processing and Classification Utilizing In Vivo Confocal Microscopy Images

Sammanfattning

The aim of this work is to enhance the detection and classification pipelines of an artificial intelligence (AI)-based decision support system (DSS) for diagnosing acanthamoeba keratitis (AK), a vision-threatening disease. The images used are taken with the in vivo confocal microscopy (IVCM) technique, a complementary tool for clinical assessment of the cornea that requires manual human analysis to support diagnosis. The DSS facilitates automated image analysis and currently aids in diagnosing AK. However, the accuracy of AK detection needs improvements in order to use it in clinical practice. To address this challenge, we utilize image brightness processing through multiscale retinex (MSR), and develop a custom-built image processing pipeline with deep learning model and rule-based strategies. The proposed pipeline replaces two deep learning models in original DSS, resulting in an overall accuracy improvement of 10.23% on average. Additionally, our improved pipeline not only enhances the original system’s ability to aid AK diagnosis, but also provides a versatile set of functions that can be used to create pipelines for detecting similar keratitis diseases.