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Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3–4, 2024, Proceedings

Research Article

Efficient Combination of Deep Learning Models for Skin Disease Detection

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-86493-3_23,
        author={Mohamed Massamba Sene and Ndeye Fatou Ngom and Michel Seck},
        title={Efficient Combination of Deep Learning Models for Skin Disease Detection},
        proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3--4, 2024, Proceedings},
        proceedings_a={INTERSOL},
        year={2025},
        month={4},
        keywords={Skin diseases identification Image segmentation Deep learning Image classification Computer vision},
        doi={10.1007/978-3-031-86493-3_23}
    }
    
  • Mohamed Massamba Sene
    Ndeye Fatou Ngom
    Michel Seck
    Year: 2025
    Efficient Combination of Deep Learning Models for Skin Disease Detection
    INTERSOL
    Springer
    DOI: 10.1007/978-3-031-86493-3_23
Mohamed Massamba Sene1,*, Ndeye Fatou Ngom1, Michel Seck1
  • 1: LTISI Laboratory
*Contact email: smmassamba@ept.sn

Abstract

Identifying skin diseases is challenging due to their similar visual appearance, making it difficult to select features. Despite significant progress in the effective identification of skin diseases, the problem remains unsolved. This paper presents a new method for accurate skin disease detection, which combines the Attention U-Net architecture for image segmentation and a customized Convolutional Neural Network (CNN) for image classification. The first step of the proposed approach is training the segmentation model on a dataset of segmented skin disease images. The resulting model is then used to segment images from a skin disease images classification dataset before training the customized CNN using the segmented images to classify skin diseases. With the proposed approach, we were able to achieve an accuracy of 99% as well as high precision, recall and F1 score of 99% on the HAM10000 dataset. Comparative analysis with other similar studies demonstrates its effectiveness in accurately identifying these diseases.

Keywords
Skin diseases identification Image segmentation Deep learning Image classification Computer vision
Published
2025-04-21
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-86493-3_23
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