A Deep Learning Application for Psoriasis Detection
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915464203993088 |
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| author | Milani, Anna da Silva, Fábio S. Guedes, Elloá B. Rios, Ricardo |
| author_facet | Milani, Anna da Silva, Fábio S. Guedes, Elloá B. Rios, Ricardo |
| contents | 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% ${\pm}$ 0.2). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18528 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Deep Learning Application for Psoriasis Detection Milani, Anna da Silva, Fábio S. Guedes, Elloá B. Rios, Ricardo Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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% ${\pm}$ 0.2). |
| title | A Deep Learning Application for Psoriasis Detection |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.18528 |