INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (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)
Unregulated skin cell development results in skin cancer. Undamaged DNA harm to skin cells causes alterations, or genetic flaws, which cause the skin cells to proliferate rapidly and develop into cancerous tumors. The look of the damaged section on the skin can be utilized to diagnose skin cancer via image analysis. One of the key areas of synthetic intellect is the artificial neural network (ANN), which is now recognized as a cutting-edge tool for picture analysis in computer science. Currently, neural nets are a hot topic in healthcare, especially in the disciplines of radiography, urology, cardiology, cancer, etc. An extremely call network requires the use of neural networks. In this research, a computational approach for applying neurons to clinical picture analysis has been devised. The final goal of this research is to analyses the clinical data and develop urgent safety nets that are affordable. In order to develop diagnosis techniques that can enhance triage procedures in the urgent care, it has been utilized to evaluate Melanoma factors including asymmetric, border, colour, and diameter (ABCD), whom are computed utilizing MATLAB from melanomas photos. We employ ANN in the classification stage using Back Propagation Algorithm using the ABCD criteria for melanoma skin cancer. We first educate the system with predetermined target values. These uncertain variables are evaluated for the categorization of cancer after the network has been trained with 96.9% accuracy. For the classification of skin cancer, this method of classification appears to be more effective.
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"Melanoma Skin Cancer Detection Using Deep Convolutional Neural Network", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 1, page no.d568-d575, January-2023, Available :http://www.ijnrd.org/papers/IJNRD2301367.pdf
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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