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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: | Precision and Progress: A 97% Accurate Model for Breast Cancer Detection |
| Authors Name: | Karthik Reddy Munnangi , Jampala Venkata Saileenath Reddy , Sai Ruthvik Reddy Aella , Kalipindi Navya , Dr. S Sri Harsha |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_211033
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| Published Paper Id: | IJNRD2312205 |
| Published In: | Volume 8 Issue 12, December-2023 |
| DOI: | |
| Abstract: | The present research study explores recent breakthroughs in the domain of breast cancer diagnosis, specifically emphasising the use of advanced deep learning algorithms. The escalating rise in breast cancer prevalence in India, characterised by the diagnosis of one woman with the ailment every two minutes and the mortality of one woman every nine minutes, highlights the pressing need for more accurate and effective diagnostic techniques. In contrast to traditional methodologies, our distinctive methodology leverages the capabilities of machine learning, resulting in a notable accuracy rate of 97%. This paper provides a complete examination of the use of deep learning and machine learning algorithms for the purpose of identifying and categorising breast cancer. This study especially focuses on the detection and differentiation of dense masses, tumours, and non-tumorous areas using several medical imaging modalities. The paper comprehensively covers several machine learning approaches, deep learning algorithms, and specialized neural network designs designed specifically for accurate diagnosis and classification of breast cancer. Furthermore, the study presents a thorough examination of the various imaging modalities and research databases that are accessible for the purposes of training and validation. This research further explores prospective advancements and challenges within the realm of breast cancer detection and therapy, emphasising the crucial significance of precise and effective detection techniques in addressing this pressing matter of public health. This study not only makes a valuable contribution to the area of medical science, but also underscores the need of early identification and diagnosis, eventually resulting in improved outcomes for individuals with breast cancer. |
| Keywords: | Deep learning, Breast cancer, machine learning, Cancer, Diagnosis |
| Cite Article: | "Precision and Progress: A 97% Accurate Model for Breast Cancer Detection", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 12, page no.c46-c51, December-2023, Available :http://www.ijnrd.org/papers/IJNRD2312205.pdf |
| Downloads: | 000118755 |
| 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:IJNRD2312205 Registration ID: 211033 Published In: Volume 8 Issue 12, December-2023 DOI (Digital Object Identifier): Page No: c46-c51 Country: Vijayawada, Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2312205 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2312205 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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