mlsanityData to 14 Oct 2026

A Deep Learning Application for Psoriasis Detection

arXiv 2508.18528 ↗First posted 25 Aug 2025eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

In this paper a comparative study of the performance of three Convolutional Neural Network models, ResNet50, Inception v3 and VGG19 for classification of skin images with lesions affected by psoriasis is presented. The images used for training and validation of the models were obtained from specialized platforms. Some techniques were used to adjust the evaluation metrics of the neural networks. The results found suggest the model Inception v3 as a valuable tool for supporting the diagnosis of psoriasis. This is due to its satisfactory performance with respect to accuracy and F1-Score (97.5% ± 0.2).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 30 references that are in the map. First posted on arXiv 25 Aug 2025.

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References · 9

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  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
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  7. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  8. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  9. 2016An overview of gradient descent optimization algorithmscited by 1,033

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