Abstract—A model-driven approach suitable for classifying microbiome-related morbidities such as ulcerative colitis on smart mobile devices is investigated in this manuscript. A novel scheme is proposed, which consists of a pre-trained image classifier on ImageNet and is deployed into the presented Android mobile application for this purpose. Endoscopic images of mouse colitis were used as input datasets for our experiments. The proposed approach offers an efficient classifier, based on the average of all its performance metrics: confusion matrix, accuracy, recall, precision, cross entropy, f1-score. The results are compared with these of the most representative image classifiers for the kind of classification we target, in terms of performance, as well as the size of the retrained frozen graph on our dataset. Such a classification could serve as a valuable tool in clinical medicine offering an automated, diagnostic tool for microbiome-related morbidities, thus allowing accurate early diagnosis and the design of personalized and targeted therapeutic approaches.

Index Terms—Classification, CNN, machine learning, microbiome, morbidity, diagnosis, colitis, mouse.

Olympia Giannou is with Computer Engineering & Informatics Dept., Polytechnic School, University of Patras, 26500, Patras, Greece.
Ahmad Mustafa Shiri is with Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), 20246, Hamburg, Germany.
Anastasios D. Giannou is with Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), 20246, Hamburg, Germany. He is also with Department of General, Visceral and Thoracic Surgery, UKE, Hamburg, Germany.
Dimitra E. Zazara is with Division for Experimental Feto-Maternal Medicine, Department of Obstetrics and Prenatal Medicine, UKE, Hamburg, Germany. She is slso with Department of Pediatrics, UKE, Hamburg, Germany.
Samuel Huber is with Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), 20246, Hamburg, Germany.
Georgios Pavlidis is with Computer Engineering & Informatics Dept., Polytechnic School, University of Patras, 26500, Patras, Greece.

Cite: Olympia Giannou, Ahmad Mustafa Shiri, Anastasios D. Giannou, Dimitra E. Zazara, Samuel Huber, Georgios Pavlidis, "A Personalized Diagnostic Tool for Microbiome-Related Morbidities," International Journal of Bioscience, Biochemistry and Bioinformatics, vol. 12, no. 3, pp. 53-62, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).