Hybrid Deep Learning Framework for Enhanced Melanoma Detection
<|reference_start|>Hybrid Deep Learning Framework for Enhanced Melanoma Detection: Cancer is a leading cause of death worldwide, necessitating advancements in early detection and treatment technologies. In this paper, we present a novel and highly efficient melanoma detection framework that synergistically combines the strengths of U-Net for segmentation and EfficientNet for the classification of skin images. The primary objective of our study is to enhance the accuracy and efficiency of melanoma detection through an innovative hybrid approach. We utilized the HAM10000 dataset to meticulously train the U-Net model, enabling it to precisely segment cancerous regions. Concurrently, we employed the ISIC 2020 dataset to train the EfficientNet model, optimizing it for the binary classification of skin cancer. Our hybrid model demonstrates a significant improvement in performance, achieving a remarkable accuracy of 99.01% on the ISIC 2020 dataset. This exceptional result underscores the superiority of our approach compared to existing model structures. By integrating the precise segmentation capabilities of U-Net with the advanced classification prowess of EfficientNet, our framework offers a comprehensive solution for melanoma detection. The results of our extensive experiments highlight the high accuracy and reliability of our method in both segmentation and classification tasks. This indicates the potential of our hybrid approach to significantly enhance cancer detection, providing a robust tool for medical professionals in the early diagnosis and treatment of melanoma. We believe that our framework can set a new benchmark in the field of automated skin cancer detection, encouraging further research and development in this crucial area of medical imaging.<|reference_end|>
@article{zhang2024hybrid,
    title={Hybrid Deep Learning Framework for Enhanced Melanoma Detection},
    author={Peng Zhang, Divya Chaudhary},
    journal={arXiv preprint arXiv:2408.00772},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00772},
    primaryClass={eess.IV cs.CV}
}