Neural Horizons: Comparison of Advanced Deep Learning Models for the Revolution in Breast Cancer Diagnosis

Registration ID: IJNRD_219603

Published ID: IJNRD2404702

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Munduku Munduku Deo , Batubenga Mwamba Nzambi Jean Didier

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...

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.

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Munduku Munduku Deo & Batubenga Mwamba Nzambi Jean Didier (April-2024). Neural Horizons: Comparison of Advanced Deep Learning Models for the Revolution in Breast Cancer Diagnosis. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), h10-h23. https://ijnrd.org/papers/IJNRD2404702.pdf

Volume 9 Issue 4, April-2024

Pages : h10-h23

Paper Reg. ID: IJNRD_219603

Published Paper Id: IJNRD2404702

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Research Area: Engineering

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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