Iranian Association of Medical InformaticsFrontiers in Health Informatics2676-710410120210430Presumptive Diagnosis of Cutaneous Leishmaniasis27827810.30699/fhi.v10i1.278ENCarlosAlbertoArce-LoperaUniversidad Icesi. email@example.comJavierDiaz-CelyLinaQuintero202102072021032320210303Introduction: Cutaneous Leishmaniasis is a neglected tropical disease caused by a parasite. The most common presumptive diagnostic tool for this disease is the visual examination of the associated skin lesions by medical experts. Here, a mobile application was developed to aid this pre-diagnosis using an automatic image recognition software based on a convolutional neural network model.Methods: 2022 images of cutaneous diseases taken from 2012 to 2018 were used for training. Then, in 2019, machine learning techniques were tested to develop an automatic classification model. Also, a mobile application was developed and tested against specialized human experts to compare its performance.Results: Transfer learning using the VGG19 model resulted in a 93% accuracy of the classification model. Moreover, on average, the automatic model performance on a randomly selected skin image sample revealed a 99% accuracy while, the ensemble prediction of seven human medical expert’s accuracy was 83%.Conclusions: Mobile skin monitoring applications are crucial developments for democratizing health access, especially for neglected tropical diseases. Our results revealed that the image recognition software outperforms human medical experts and can alert possible patients. Future developments of the mobile application will focus on health monitoring of Cutaneous Leishmaniasis patients via community leaders and aiming at the promotion of treatment adherence.
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