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Detection of Melanoma Insitu Using Trained CNN Model

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Abstract

Being the most deadly kind of skin cancer, melanoma presents a serious risk to life and is increasingly prevalent, particularly among men. It is characterized by aggressive cell multiplication that can spread within the body if left untreated, making early detection crucial for successful treatment. While machine learning techniques have been used for melanoma diagnosis, they often have limitations in performance. To address this, we propose an advanced deep learning framework incorporating a novel hybrid architecture that combines Convolutional Neural Networks (CNNs) with attention mechanisms. This framework also incorporates advanced techniques for model interpretability, such as visualizing the areas of the image that the model focuses on for its decision-making, to enhance transparency and trustworthiness for medical professionals and leverages transfer learning with pre-trained networks to boost performance. We utilized a range of optimization techniques, including AdaBelief and dynamic learning rates, to enhance model training. Our evaluation metrics included accuracy, sensitivity, specificity, and precision. Comparative results demonstrated that our model outperformed traditional techniques, with the ResNet50 architecture combined with the Adam optimizer achieving an accuracy of 92%, sensitivity of 90%, and specificity of 94%. These findings demonstrate the efficacy of our deep learning strategy by showing a notable improvement in melanoma detection performance. It is imperative to underscore that actual clinical trials or pilot studies are required to validate the model's effectiveness and reliability in real-world scenarios.

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Data Availability

Labeled datasets used to support the findings of this investigation can be obtained from the corresponding author upon request.

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Acknowledgements

Authors acknowledge the support from REVA University, Bengaluru- 560064, Manipal Institute of Technology Bengaluru, and VelTech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai for the facilities provided to carry out the research.

Funding

There are no particular grants from funding organizations for this research.

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Authors and Affiliations

  1. School of Computer Science Engineering, REVA University, Bengaluru, 560064, India

    R. SethuMadhavi, T. Y. Satheesha & P. N. Asha

  2. Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education (MAHE), Bangalore, Karnataka, 560064, India

    Anitha Premkumar

  3. School of Science and Technology, Department of Engineering, University of London, London, United Kingdom

    T. Y. Satheesha

  4. School of Computing and Information Technology, REVA University, Bengaluru, 560064, India

    B. Bhasker

  5. Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, India

    M. DharmaTheja

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Correspondence to Anitha Premkumar.

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SethuMadhavi, R., Premkumar, A., Satheesha, T.Y. et al. Detection of Melanoma Insitu Using Trained CNN Model. SN COMPUT. SCI. 5, 984 (2024). https://doi.org/10.1007/s42979-024-03326-x

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