Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

Early pigment spot segmentation and classification from iris cellular image analysis with explainable deep learning and multiclass support vector machine

Globally, retinal disorders impact thousands of individuals. Early diagnosis and treatment of these anomalies might halt their development and prevent many people from developing preventable blindness. Iris spot segmentation is critical due to acquiring iris cellular images that suffer from the off-angle iris, noise, and specular reflection. Most currently used iris segmentation techniques are based on edge data and noncellular images. The size of the pigment patches on the surface of the iris increases with eye syndrome. In addition, iris images taken in uncooperative settings frequently have negative noise, making it difficult to segment them precisely. The traditional diagnosis processes are costly and time consuming since they require highly qualified personnel and have strict environments. This paper presents an explainable deep learning model integrated with a multiclass support vector machine to analyze iris cellular images for early pigment spot segmentation and classification. Three benchmark datasets MILE, UPOL, and Eyes SUB were used in the experiments to test the proposed methodology. The experimental results are compared on standard metrics, demonstrating that the proposed model outperformed the methods reported in the literature regarding classification errors. Additionally, it is observed that the proposed parameters are highly effective in locating the micro pigment spots on the iris surfaces.