Efficient strabismus diagnosis from small samples: Harnessing spatial features for improved accuracy.

Abstract

Strabismus is a common ophthalmological condition, and early diagnosis is crucial to preventing visual impairment and loss of stereopsis. However, traditional methods for diagnosing strabismus often rely on specialized ophthalmic equipment and trained personnel, limiting the widespread accessibility of strabismus diagnosis. Computer-aided strabismus diagnosis is an effective and widely used technology that assists clinicians in making clinical diagnoses and improving efficiency. To address this, we designed an efficient strabismus diagnosis model, RIS-MLP, based on a small number of samples derived from frontal facial images captured under natural lighting conditions via the Hirschberg test. The RIS-MLP combines light reflex point detection and iris detection modules to accurately extract key spatial features even under noisy and occluded conditions. The optimized spatial feature strategies further enhances the performance of the classification module. To validate the superiority of RIS-MLP, we conducted both direct and indirect comparative experiments. Indirect comparisons demonstrate that the RIS-MLP has advantages in terms of sample efficiency. While direct comparisons show that the RIS-MLP can mitigate overfitting to a certain extent, and the RIS-MLP along with its variants (e.g., RIS-SVM) have outperformed state-of-the-art models on our noisy and imbalanced dataset.