Adaptive feature selection for active trachoma image classification

Abstract

Trachoma is a neglected tropical eye disease caused by ocular strains of Chlamydia trachomatis, which affects millions of people worldwide. To examine the eye for signs of active trachoma, healthcare providers typically look for clusters of five or more follicles on the conjunctiva of the upper eyelid for the follicular inflammatory trachoma stage. However, it is also possible to find individual follicles scattered throughout the conjunctiva, particularly in mild or early-stage trachoma cases. Additionally, the datasets are photographic images collected in the field that can be high-dimensional and may contain large amounts of redundant information. We propose integrating novel attention-based feature extraction and feature selection techniques to address these challenges. First, we present the Lambda layer within the Convolutional Block Attention Module (L-CBAM) to normalize attention weights and improve the feature extraction process. Second, we introduce an adaptive mechanism, Adaptive Beta Hill Climbing (AβHC) with Social Ski-Driver (SSD), which adjusts the exploration-exploitation trade-off during the search process, allowing for better exploration of the search space and more efficient convergence toward an optimal feature subset. We then use the multilayer perceptron (MLP) classifier to produce final classification results using selected subsets. We evaluated the proposed approach on active trachoma inverted eyelid images and obtained accuracy scores of 93.3% with only 19.7% of the selected features, surpassing many of the algorithms used for comparison. Our proposed method has demonstrated excellent performance compared to recent works utilizing the same datasets. The source code of this work is available at https://github.com/mshitie2/Active_Trachoma.