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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Lesion Border Detection of Skin Cancer Images Using Deep Fully Convolutional Neural Network with Customized Weights.

R Kaur, H Gholam Hosseini, R Sinha

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an improved fully convolutional neural network (FCNN) for segmenting skin cancer lesions in dermoscopic images. The novel FCNN architecture achieved superior accuracy and Jaccard index compared to leading methods in skin cancer detection.

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    Area of Science:

    • Medical Image Analysis
    • Deep Learning
    • Computational Pathology

    Background:

    • Skin cancer poses a significant health threat, necessitating early detection for improved patient outcomes.
    • Accurate segmentation of skin lesions from dermoscopic images is crucial for understanding tissue and cancer cell formation.
    • Deep learning, particularly semantic segmentation, shows promise in analyzing complex medical image patterns.

    Purpose of the Study:

    • To develop and evaluate an improved fully convolutional neural network (FCNN) for precise lesion segmentation in dermoscopic images of skin cancer.
    • To enhance the understanding of tissue and cancer cell formation through accurate segmentation of cancerous areas.
    • To provide a tool that aids experts in the diagnosis and analysis of skin cancer.

    Main Methods:

    • Proposed a novel fully convolutional neural network (FCNN) architecture specifically designed for semantic segmentation of skin lesions.
    • Incorporated stacked feature extraction layers and customized convolutional weights to generate high-resolution feature maps.
    • Compared the proposed FCNN model against top-performing methods from the International Skin Imaging Collaboration (ISIC) challenge.

    Main Results:

    • The improved FCNN model demonstrated superior performance in lesion segmentation tasks.
    • Achieved higher accuracy and Jaccard index values compared to the four leading competitors in the ISIC challenge.
    • The customized network architecture and weight generation proved effective in producing precise pixel-level segmentation.

    Conclusions:

    • The proposed improved FCNN architecture offers a significant advancement in the semantic segmentation of skin cancer lesions.
    • This method enhances the accuracy of lesion identification, supporting dermatologists in clinical decision-making.
    • The study highlights the potential of tailored deep learning models for improving medical image analysis in oncology.