Detection of Diabetes Mellitus With Deep Learning and Data Augmentation Techniques on Foot Thermography

Abstract

This research was supported by the research division from BASPI FOOTLAB (https://ingenieria.javeriana.edu.co/investigacion/laboratorio-footlabb) and INDIGO Technologies (https://indigo.tech/ ).

Development of Image Based Model for Basic Standing Yoga Poses that Control Type-2 Diabetes

“…S El-Sappagh et.al implemented a semantically interpretable fuzzy rule based system framework using knowledge fuzzy inference, ontology reasoning and fuzzy analytical hierarchy process for diagnosis of diabetes [21]. Y Zhou et.al had developed a dataset with 1842 large fine grained annotated diabetic retinopathy images [22]. Authors in [23][24][25] proposed algorithms to overcome the considerations of missing and imbalanced data about diabetes.…”

Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

“…When the historical advancements within Artificial Intelligence (AI) is examined, it can be seen that Deep Learning models dominate the recent medical applications, as resulting successful outcomes even in the most critical diagnosis and treatment research cases [9][10][11][12][13]. Deep Learning-based solutions, which reveal more successful findings than traditional Machine Learning solutions (especially in sensitive analysis of image data) enable effective approaches to be designed in the stages of tumor detection, brain hemorrhage diagnosis, early prediction of eye diseases and many different problems in an automatic way [14][15][16][17][18][19]. At this point, the advanced Deep Learning models may be used to have efficiency in terms of time and resources and the physiotherapists may benefit from a complete decision support system, if the appropriate set-up may be done with specific algorithmic touches.…”