Authors - Ana Laura Lezama Sanchez, Mireya Tovar Vidal Abstract - In this paper, we present the automatic classification of autoimmune skin diseases using deep convolutional neural networks. Hence in this study we conducted within the context of supervised classification of dermatological images, with the objective of designing and implementing a model capable of distinguishing among five clinical classes like lupus, psoriasis, vitiligo, lichen planus and healthy skin. Therefore, a deep convolutional neural network-based system, trained and evaluated on a labeled dataset of clinical images, is proposed. The model was evaluated using the metrics precision, recall, F1 and accuracy. The results obtained indicated that the accuracy was 70%, demonstrating the model’s ability to learn relevant discriminattive features. The best performance was observed in the healthy skin and vitiligo classes, with F1 of 0.82 and 0.80, respectively, indicating high identification capacity. On the other hand, the psoriasis and lichen planus classes showed moderate performance, with F1 values of 0.63 and 0.58, respectively. The lupus class exhibited the lowest performance, with an F1 of 0.46, reflecting the complexity of its visual variability and its similarity to other conditions.
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