Superpixel-based Landmark Identification and Disease Diagnosis from Gastrointestinal Images

Abstract

Gastrointestinal (GI) diseases are among the most frequently occurring diseases that pose a significant threat to people's health. The gold standard for diagnosing these diseases is endoscopic examination, yet this approach is resource-intensive, requiring costly equipment and specialized training. This study explores an alternative approach for GI image segmentation and classification, employing Simple Linear Iterative Clustering (SLIC) and Linear Spectral Clustering (LSC) superpixel methods. Analyzing images from the comprehensive Kvasir dataset, which represents different GI tract sections, the research applies three distinct features-local binary pattern, gray-level co-occurrence matrices, and first-order statistical features-for Support Vector Machine (SVM) classification. The results demonstrate that superpixel-based classification methods exceed traditional pixel-wise techniques in terms of accuracy and efficiency. Specifically, SLIC excels in upper GI tract analysis, yielding 77.33% accuracy, 77.89% sensitivity, and 76.8% specificity. Conversely, LSC shows superior performance for middle and lower GI sections, with accuracy, sensitivity, and specificity of 98.5%, 100%, and 97.1% for the middle GI, and 93.67%, 91.72%, and 95.8% for the lower GI tract, respectively. Moreover, SLIC operates faster than LSC. These findings highlight superpixel methods' potential to improve GI disease diagnosis, promising more efficient, accurate medical imaging.