Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencoders

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Coronado R.
Ocsa A.
Quispe O.
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Springer Verlag
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Every year, people around the world are affected by different skin diseases or cancer. Nowadays, these can only be detected accurately by clinical analysis and skin biopsy. However, the diagnosis of this malignant disease does not ensure the survival of the patient, since many clinical cases are detected in the terminal phases. Only early diagnosis would increase the life expectancy of patients. In this paper, we propose a method to recognition malignant skin diseases to identify malignant lesions in non-dermatoscopic images. For the method, we use Convolutional Neural Network and propose the use of autoencoders as another classification model that provides more information on the diagnosis. Experiments show that our proposal reaches up to 84.4% of accuracy in the well-known dataset of the ISIC-2016. In addition, we collect non-dermatoscopic images of skin lesions and developed a new dataset to demonstrate the advantage of our method. © Springer International Publishing AG, part of Springer Nature 2018.
This work has been partially funded by the Master Scholarship at the Universidad Nacional de San Agustín, which is an initiative of CITEC through a fund FONDECYT (Perú). We would like to thank research department of Instituto Nacional de Enfermedades Neoplásicas from Peru, for gently providing us his advice on the direction of this article.
Palabras clave
Skin cancer, Autoencoders, Convolutional neural networks, Image classification