Investigation on Image Based Diagnostic Approach for Throat Related Symptom Through Feature Extraction Using MATLAB

Early diagnosis and treatment of these symptoms are essential to prevent complications and ensure a prompt recovery. Traditional throat examinations can also be distressing for young children, delaying care-seeking and complicating clinical assessment. The aim of this paper is to investigate image detection analysis of image-based diagnostic approach for pediatric throat symptoms using MATLAB-based image analysis, offering a child-friendly screening tool. Regions of Interest (ROI) marking and feature extraction are used to analyze images of children’s throats, captured using widely available endoscopic cameras and transferred to MATLAB software for processing. The analysis pipeline measures contrast, RGB (Red, Green, and Blue) channel intensities, and overall image quality to determine suitability for further diagnostic analysis. Red and white ROIs are then marked to highlight throat conditions such as redness and swelling, and feature extraction techniques including edge detection and circle detection. Hence, abnormal patterns such as inflamed tonsils have been identified. Real images of children’s throat aged three to ten years old are compared with virtual healthy throat images representing infected cases to evaluate performance analysis. Results show that both the red and white ROIs were accurately detected, enabling targeted examination of specific throat regions. Image captured by smartphone and endoscopic hardware other than specialized clinical equipment provide significance result to throat symptom screening, supporting non-specialist healthcare workers and reducing the burden of invasive examinations on young patients. The findings suggest that accessible image-based diagnostic tools can extend basic throat-screening capability to underserved pediatric populations while easing the diagnostic process for physicians.

Throat, Regions of Interest (ROI), Feature Extraction, Image Detection

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