Synergetic Fusion Of CNN-SVM And CNN-RF For Enhanced Leukemia Detection In Biomedical Applications
DOI:
https://doi.org/10.29284/ezsskc11Keywords:
Leukemia, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest (RF), Medical imaging, feature extraction, hybrid models, classification, deep learning, machine learning.Abstract
In medical diagnostics, leukemia identification is a crucial where precise and effective approaches are essential for prompt treatment and prognosis. In this article, we present a novel method, i.e., Convolutional Neural Networks (CNN) in conjunction with Support Vector Machines (SVM) and Random Forest (RF) classifiers for leukemia identification. Our technique extracts discriminating features from medical imaging data by utilizing CNNs' potent feature learning capabilities. Furthermore, the collected characteristics are then applied to RF and SVM classifiers to be classified. The hybrid CNN-SVM and CNN-RF models leverage the complimentary advantages of conventional machine learning methods and deep learning to enhance both interpret-ability and excellent classification accuracy. We also test the suggested approach using a public dataset of ALL IDB1 and ALL IDB2. Performance metrics including accuracy, precision, recall, F1-score, the results demonstrate the efficacy of the CNN-SVM and CNN-RF models for Leukemia detection, showcasing their potential as reliable tools for aiding clinicians in diagnosing this life-threatening disease. The CNN-RF outperforms with accuracy of 95% compared to CNN-SVM.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
