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Skin cancer segmentation with the aid of multi-class dilated D-net (MD2N) framework

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Abstract

Skin cancer is one of the world’s most hazardous diseases, and identification of skin cancer is more challenging. Recently Deep learning algorithms have evolved to produce outstanding outcomes in a wide range of clinical practices. But classifying different classes of skin cancer is complex due to the fine-grained variability of skin lesion regions. Based on this insight, the current work considers an early and accurate multi-class skin cancer classification issue by introducing a novel deep learning model. As typical deep learning models have fewer receptive fields, they are unable to gather global context information from wider regions, making it difficult to identify diseased areas. Furthermore, in previous studies, the segmented skin lesion region caused higher noise and worse classification accuracy. To overcome the existing problem, a novel Multi-class Dilated D-Net (MD2N) framework is introduced to segment and classify multiple classes of skin cancer screening. The encoder phase of the proposed MD2N reduces feature information losses by adopting a downsampling ratio while also distinguishing small skin lesion patches. Dilated manifold Parallel convolution effectively expands the network’s receptive field and eliminates the “grid issue” that plagues ordinary dilated convolution. As a result, the model can get more rich feature information of skin lesions areas of dissimilar sizes. Further, the proposed deep learning model initiates the system to accurately segment and classify lesion regions from the input skin image obtained from International Skin Imaging Collaboration.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India

    Mikkili Dileep Kumar & D.N.V.S.L.S. Indira

  2. Department of Computer Science, GIT, GITAM University, Visakhapatnam, Andhra Pradesh, India

    G V Sivanarayana

  3. Department of Computer Science, NRI Institute of Technology, Guntur, Andhra Pradesh, India

    M. Pruthvi Raj

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  1. Mikkili Dileep Kumar
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  3. D.N.V.S.L.S. Indira
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Correspondence to Mikkili Dileep Kumar.

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Kumar, M.D., Sivanarayana, G.V., Indira, D. et al. Skin cancer segmentation with the aid of multi-class dilated D-net (MD2N) framework. Multimed Tools Appl 82, 35995–36018 (2023). https://doi.org/10.1007/s11042-023-14605-9

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