Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This paper introduces a novel deep learning framework for accurately segmenting skin
lesions and detecting melanoma in dermatology images. Leveraging convolutional neural networks
(CNNs), the framework achieves precise lesion segmentation and subsequent classification into
benign and malignant categories. Using a diverse dataset, the study demonstrates the effectiveness
of the proposed approach through high precision and recall rates in both segmentation and
melanoma detection tasks. The framework has the potential to be seamlessly integrated into clinical
practice, aiding dermatologists in improving the efficiency of skin cancer diagnosis and treatment
planning.
"Skin lesion segmentation and melanoma detection in dermatology images deep learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 4, page no.361 - 364, April-2024, Available :http://www.ijrti.org/papers/IJRTI2404050.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator