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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: | Neural Horizons: Comparison of Advanced Deep Learning Models for the Revolution in Breast Cancer Diagnosis |
| Authors Name: | Munduku Munduku Deo , Batubenga Mwamba Nzambi Jean Didier |
| Download E-Certificate: | Download |
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IJNRD_219603
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| Published Paper Id: | IJNRD2404702 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | In this groundbreaking study, we orchestrated a meticulous comparison of four revolutionary deep learning architectures: the Multilayer Perceptron (MLP), the Convolutional 1D Neural Network (Conv1D), the Recurrent Neural Network (RNN) and the Long Term Short Term Memory (LSTM). We have thus deployed their disruptive potential for cutting-edge breast cancer diagnosis. Drawing on the Wisconsin Breast Cancer Database (WBCD) and the Breast Cancer Coimbra Database (BCC), our research not only optimised hyperparameters via Grid Search CV but also incorporated cross-validation, paving the way for a new era in diagnostic reliability and robustness. Our exploration revealed exceptional performance on WBCD, MLP and Conv1D leading the way with spectacular accuracies of 99.30% and 96%, near-perfect F1 scores of 0.99 and 0.96, and ideal AUCs of 1.00. The RNN and LSTM models followed with distinction, displaying accuracies of 97.20% and 98.60%, F1 scores of 0.97 and 0.98, and AUCs of 1.00 and 0.99 respectively,Concerning the BCCD, the models demonstrated remarkable adaptability and performance. MLP shone with an accuracy of 80.77%, an F1 score of 0.80, and an AUC of 0.88, while Conv1D, RNN, and LSTM presented accuracies of 81%, 84.62%, and 84.62%, with F1 scores of 0.78, 0.82, and 0.83, and AUCs of 0.88, 0.89, and 0.81.This research represents a significant leap towards the optimal use of deep learning to save human lives. |
| Keywords: | Deep learning, Multilayer Perceptron (MLP), Convolutional Neural Network 1D (Conv1D), Recurrent Neural Network (RNN), Long Term Short Term Memory (LSTM), Wisconsin Breast Cancer Database (WBCD), Breast Cancer Coimbra Database (BCCD), Medical diagnosis... |
| Cite Article: | "Neural Horizons: Comparison of Advanced Deep Learning Models for the Revolution in Breast Cancer Diagnosis", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.h10-h23, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404702.pdf |
| Downloads: | 00040 |
| 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:IJNRD2404702 Registration ID: 219603 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: h10-h23 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404702 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404702 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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