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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) |
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| Paper Title: | Unveiling Dermatological Threats: Deep Learning-Based Skin Cancer Classification from Lesion Images |
| Authors Name: | Dr.P.Manikandaprabhu , Mr.V.Pradish |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_216524
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| Published Paper Id: | IJNRD2403634 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | In the present-day period, pores and skin disorders have emerged as major health issues, necessitating advanced analysis techniques. Partitioning the lesion vicinity is a vital step in deep learning-based computer-aided analysis, which has become famous as a beneficial device to help doctors in diagnosing patients. However, good-sized pixel-level labeling is generally wanted for completely supervised training with conventional scientific image segmentation techniques, that is a hard and specialized understanding-extensive manner. This novel approach to pores and skin lesion area segmentation that simplest makes use of photo-stage labels to solve those issues and reduces the costs of pixel-stage labeling. The purpose of this study is to research and determine strategies designed specifically for the identity of pores and skin melanoma. The goal is to evaluate the efficacy and relevance of those various strategies in improving the sector of cancer detection and prognosis. In cutting-edge times, skin illnesses pose sizable fitness concerns, driving the need for advanced diagnostic equipment. This novel method is proposed that utilizes photo-stage labels to segment skin lesion regions, decreasing the want for extensive pixel-stage labeling. The study focuses on evaluating approaches customized for the detection of skin melanoma, including traditional dermatoscopic image analysis and overall body assessments. Through an exploration of these methods, the aim is to assess their effectiveness in enhancing melanoma detection and diagnosis. |
| Keywords: | Benign lesions, Convolutional neural network (CNN), Deep learning, DenseNet, ISIC archive, Malignant lesions, VGG16 |
| Cite Article: | "Unveiling Dermatological Threats: Deep Learning-Based Skin Cancer Classification from Lesion Images", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.g287-g292, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403634.pdf |
| Downloads: | 00041 |
| ISSN: |
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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Published Paper ID:IJNRD2403634 Registration ID: 216524 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: g287-g292 Country: Coimbatore, Tamil Nadu, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403634 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403634 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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