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Hybrid of Convolutional Neural Network and Support Vector Machine for Cancer Type Prediction | ||
| Control and Optimization in Applied Mathematics | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 13 اسفند 1403 اصل مقاله (511.98 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.30473/coam.2025.72710.1269 | ||
| نویسنده | ||
| Soghra Mikaeyl Nejad* | ||
| Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran. | ||
| چکیده | ||
| Gene expression signatures reflect the response of cell tissues to diseases, genetic disorders, and drug treatments, containing hidden patterns that can provide valuable insights for biological research and cancer diagnostics. This studyproposes a hybrid deep learning approach combining convolutional neural networks (CNNs) and support vector machines (SVMs) to classify cancer types using unstructured gene expression data. We applied three hybrid CNN-SVM models to a dataset of 10,340 samples spanning 33 cancer types from the Cancer Genome Atlas. The CNN component extracted latent features from the gene expression data, while the SVM replaced the softmax layer to enhance classification robustness. Among the proposed models, the Hybrid-CNN-SVM model achieved superior performance, demonstrating excellent prediction accuracy and outperforming other models. This study highlights the potential of hybrid deep learning frameworks for cancer type prediction and underscores their applicability to high-dimensional genomic datasets. | ||
تازه های تحقیق | ||
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| کلیدواژه‌ها | ||
| Deep learning؛ Convolutional neural networks؛ Support vector machine؛ Gene expression؛ The cancer genome atlas؛ Cancer type prediction | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 34 تعداد دریافت فایل اصل مقاله: 13 |
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