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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: | Advancing Ovarian Cancer Diagnosis: A Multifaceted Deep Learning Approach for Automated Prediction and Subtype Classification |
| Authors Name: | Sukeshini Vijay Jadhav , Vijayshri Injamuri , Sudhir Shikalpure |
| Download E-Certificate: | Download |
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IJNRD_216775
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| Published Paper Id: | IJNRD2403579 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | http://doi.one/10.1729/Journal.38636 |
| Abstract: | Early detection of ovarian cancer is crucial for effective treatment and improved patient outcomes. In this paper, we proposed a novel deep learning-based approach for accurate ovarian cancer classification and segmentation, utilizing advanced convolutional neural network (CNN) architectures. Our approach integrates segmentation and classification within a unified framework, facilitating comprehensive analysis and precise identification of ovarian cysts. Through extensive experimentation and analysis, we demonstrate remarkable accuracy rates ranging from 95% to 98%, surpassing existing methods in the field. The suggested method uses the conventional VGG-16 model, which has been refined using a dataset comprising 3457 real patient photos, including a private dataset of 1616 ultrasound pictures. Ultrasound imaging can identify ovarian cysts, which provide serious health issues such as infertility and torsion. Our model effectively distinguishes between ultrasound pictures showing the existence of ovarian cysts and those that do not by altering the final four layers of the VGG-16 network. Our research offers a dependable and effective technique for early identification of ovarian cancer, advancing gynecological oncology research and clinical practice. Our strategy has great promise to improve patient outcomes and lower mortality rates related to ovarian cancer by utilizing deep learning techniques and merging segmentation and classification methodologies. |
| Keywords: | Ovarian cancer, Ovarian Cyst, CNN, Deep-Learning, Subtype, Classification, Automatic prediction, Ultrasound Image Processing, Artificial Intelligence, Women’s Healthcare ,Innovation using ai |
| Cite Article: | "Advancing Ovarian Cancer Diagnosis: A Multifaceted Deep Learning Approach for Automated Prediction and Subtype Classification", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.f688-f696, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403579.pdf |
| Downloads: | 00071 |
| 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:IJNRD2403579 Registration ID: 216775 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.38636 Page No: f688-f696 Country: Chhatrapati Sambhajinagar (M Corp.), Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403579 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403579 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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