Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/1539 2019 2019 Delta Higher Institute for Engineering and technology, Mansoura, Egypt Reem N. Yousef Delta Higher Institute for Engineering and technology, Mansoura, Egypt Marwa M. Eid Electronics and communications Engineering, Faculty of Engineering, Mansoura University, Egypt Mohamed A. Mohamed Diabetic foot (DF) is one of the most common chronic complications of poorly controlled diabetes mellitus (DM). Early diagnosis of DF and effective treatment is usually difficult by traditional approaches. Lately, it has been found a strong relationship between temperature variation and diabetic foot ulcer emergence. Thus, the current study focused on monitoring the temperature of feet using thermal images and its analysis techniques. The proposed system was based on employing a deep convolutional neural network (CNN) on thermal foot images. Experimental results showed that the proposed CNN has a maximum accuracy of 99.3% with minimum losses. When comparing the proposed system to other relevant systems, the proposed system approved greater accuracy, lower elapsed and testing time, which offers an automatic diagnostic tool for the diabetic foot and differentiates between its types. Thus, a simple, cost-effective, and accurate computer aided design (CAD) system could be presented to get a valuable system for the clinicians in hospitals. 2023 2023 17 32 10.54216/JISIoT.080102 https://www.americaspg.com/articleinfo/18/show/1539