Abstract
It is a twenty-first-century disease, its numbers are still growing exponentially. This brings one to the subject of this work, the Maturing of Diabetic Foot which, like diabetes, rises to values never seen before. It is envisaging the development of an ImageJ plug-into extract relevant feature from diabetic foot images and, in conjunction with the patient’s clinical and lifelong data, a computational system to predict and evaluate its severity. The applied problem-solving method is based on a symbolic/sub-symbolic line of logical formalisms that make complex systems easier to develop and analyze, where solutions to new problems are based on answers to previous ones, and itemized as a Case-Based Reasoning/Artificial Neural Network approach to computing.
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Neves, J. et al. (2019). Predicting Diabetic Foot Maturing Through Evolutionary Computation. In: Peng, SL., Dey, N., Bundele, M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-13-7150-9_11
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DOI: https://doi.org/10.1007/978-981-13-7150-9_11
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