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Towards improved U-Net for efficient skin lesion segmentation

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Abstract

Skin cancer is a highly lethal disease, and detecting it at an early stage is critical. Skin lesion segmentation is a complex process involving identifying the infected area in an image with low contrast, variable size, and position. This task is essential in medical analysis, as it helps clinicians focus on a specific area of the image before further analysis. Our paper introduces a new method for improving the segmentation of medical images by providing the efficient neural connections to design efficient U-Net architecture. We have utilized skip paths to the encoder and minimize the semantic gap between concatenated feature maps. This leads to more precise segmentation outcomes. We have used the PH2 and ISIC-2018 as benchmark dataset to validate the effectiveness of the proposed approach and surpass the available benhcmark performance. We have obtained approximately 96.18% accuracy with the PH2 dataset and 96.09% accuracy with the ISIC-2018 dataset. The outcomes of our architecture are quite impressive, and they exhibit superior performance over both the baseline model and other state-of-the-art techniques.

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Availability of data

Datasets are available at https://www.kaggle.com/datasets/synked/ph2-modified and https://www.kaggle.com/datasets/tschandl/isic2018-challenge-task1-data-segmentation

Code availability

Available at https://github.com/kishorebabun/Code_Journal4.

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Acknowledgements

This research was made possible thanks to the generous support of the MHRD grant (Grant No: OH-31-24-200-428) from IIT Roorkee.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Kishore Babu Nampalle, Anshul Pundhir, Pushpamanjari Ramesh Jupudi & Balasubramanian Raman

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  1. Kishore Babu Nampalle
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  2. Anshul Pundhir
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Contributions

Kishore Babu Nampalle: Data collection and processing, Implementation, Methodology, Experiments, Result Analysis, Writing Manuscript. Anshul Pundhir: Data Processing, Conceptualization, Writing Manuscript. Pushpamanjari Ramesh Jupudi: Writing Manuscript and editing, Validation, Writing - a review. Balasubramanian Raman: Conceptualization, Writing - review, Supervision, Administration.

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Correspondence to Kishore Babu Nampalle.

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Nampalle, K.B., Pundhir, A., Jupudi, P.R. et al. Towards improved U-Net for efficient skin lesion segmentation. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18334-5

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