Autoimmune gastritis detection from preprocessed endoscopy images using deep transfer learning and moth flame optimization

• Published in: Scientific reports Vol. 15; no. 1; pp. 24940 - 15
• Main Authors: Almutairi, Fadiyah M., Althubiti, Sara A., Aslam, Shabnam Mohamed, Dhahri, Habib, Alhajlah, Omar, Mittal, Nitin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/4077; Algorithms; Artificial intelligence; Autoimmune Diseases - diagnosis; Autoimmune Diseases - diagnostic imaging; Autoimmune gastritis; Classification; Clinical significance; Deep Learning; Disease; Endoscopy; Endoscopy images; Entropy; Entropy thresholding; Female; Gastritis; Gastritis - diagnosis; Gastritis - diagnostic imaging; Humanities and Social Sciences; Humans; Illnesses; Image Processing, Computer-Assisted - methods; Literature reviews; Medical screening; Moth-Flame algorithm; multidisciplinary; Patients; Science; Science (multidisciplinary); Stomach; Transfer learning; Autoimmune gastritis; Entropy thresholding; Moth-Flame algorithm; Deep-learning; Endoscopy images; Classification
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-08249-y

Gastric Tract Disease (GTD) constitutes a medical emergency, emphasizing the critical importance of early diagnosis and intervention to lessen its severity. Clinical practices often utilize endoscopy-supported examinations for GTD screening. The images obtained during this procedure are examined to identify the presence of the disease and investigate its severity. Autoimmune Gastritis (AIG) is a chronic inflammatory GTD and timely detection and treatment is crucial to reduce its harshness. This research aims to develop a deep-learning (DL) tool to detect the AIG from clinical-grade endoscopic images. Various stages in the DL tool comprise; (i) Image collection and resizing, (ii) image pre-processing using Entropy-function and Moth-Flame (MF) Algorithm, (iii) deep-features extraction using a chosen DL-model, (iv) feature optimization using MF algorithm and serial features concatenation, and (iv) classification and performance confirmation using five-fold cross-validation. This study aims to develop a DL tool to assist clinicians during the AIG examination and hence better detection accuracy is preferred. The merit of the DL model is demonstrated in the individual deep-features and serially concatenated-features and the experimental outcome of this study provides a detection accuracy of 99.33% when the detection is performed with fused-features and K-Nearest Neighbor classifier. This authenticates that this tool offers a clinically important outcome on the endoscopy database.