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Multi-feature Fusion Deep Network for Skin Disease Diagnosis

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Abstract

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance.

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Data Availability

The three datasets used in this work are publicly available, and one private dataset is available upon request.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, India

    Ajay Krishan Gairola & Ashok Kumar Sahoo

  2. Department of Computer Science and Engineering, Graphic Era deemed to be university, Dehradun, India

    Ajay Krishan Gairola, Vidit Kumar & Manoj Diwakar

  3. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

    Prabhishek Singh

  4. School of Computer Science and Artificial Intelligence, SR University Warangal Telangana, 506371, Warangal, India

    Deepak Garg

Authors
  1. Ajay Krishan Gairola
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  2. Vidit Kumar
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  3. Ashok Kumar Sahoo
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  4. Manoj Diwakar
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  5. Prabhishek Singh
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Correspondence to Manoj Diwakar.

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Gairola, A.K., Kumar, V., Sahoo, A.K. et al. Multi-feature Fusion Deep Network for Skin Disease Diagnosis. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18958-7

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  • DOI: https://doi.org/10.1007/s11042-024-18958-7

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