تصنيف أورام الدماغ باستخدام التعلم العميق التجميعي المعتمد على تقنية التصويت بالأغلبية

المؤلفون

  • مطيع السماوي قسم الهندسة الطبية الحيوية، جامعة العلوم والتكنولوجيا، صنعاء، اليمن
  • وليد حسين العرشي قسم الهندسة الإلكترونية، جامعة العلوم والتكنولوجيا، صنعاء، اليمن
  • محمد محسن الخولاني قسم الهندسة الإلكترونية، جامعة العلوم والتكنولوجيا، صنعاء، اليمن

DOI:

https://doi.org/10.59222/ustjet.4.1.4

الكلمات المفتاحية:

تصنيف أورام الدماغ، التعلّم العميق، التعلّم التجميعي، الشبكات العصبية الالتفافية، تقنية التصويت، التصوير بالرنين المغناطيسي

الملخص

يُعدّ تصنيف أورام الدماغ عملية حاسمة في مجال التشخيص الطبي لآفات الدماغ، لما له من دور كبير في الوصول إلى تشخيص دقيق والبدء في تخطيط العلاج. في هذه الدراسة، تم اقتراح منهجية تعلّم تجميعي بالاعتماد على نموذج VGG19 بهدف تحقيق دقة عالية في التصنيف. حيث جرى تدريب ثلاثة نماذج من VGG19 بمَعلمات مختلفة بشكل مستقل، ثم دمج تنبؤاتها باستخدام أسلوب التعلّم التجميعي المعتمد على تقنية التصويت. تم تدريب النماذج على قاعدة بيانات شاملة من صور الرنين المغناطيسي للدماغ، تضم 7,041 صورة MRI، قُسمت بنسبة 80% للتدريب و20% للاختبار. وتمت إعادة تحجيم بيانات التدريب الأولية قبل تمريرها لكل نموذج من النماذج الثلاثة بشكل منفصل. كما خضع كامل بيانات الاختبار لعمليات معالجة مسبقة باستخدام تقنيات زيادة حدّة الصور بهدف تعزيز تفاصيل صور الدماغ. وقد حقق النهج المقترح دقة عالية بلغت 99.33% على بيانات الاختبار، متفوقًا على أحدث الأساليب في مجال تصنيف أورام الدماغ. إضافة إلى ذلك، سجّل النهج المقترح نتائج عالية في الدقة (Precision) والخصوصية (Specificity) والاسترجاع (Recall) ودرجة F1، مما يدل على كفاءته. وتُظهر النتائج فعالية هذا النهج مقارنة بالنماذج الفردية، مع تحسينات كبيرة حققها التعلّم التجميعي. تُسهم هذه الدراسة في مجال التشخيص الطبي من خلال تقديم إطار دقيق لتصنيف أورام الدماغ تلقائيًا.

المراجع

[1] V. K. Dhakshnamurthy, M. Govindan, K. Sreerangan, M. D. Nagarajan, and A. Thomas, "Brain Tumor Detection and Classification Using Transfer Learning Models," Engineering Proceedings, vol. 62, no. 1, p. 1, 2024.

[2] M. A. Hamid and N. A. Khan, "Investigation and classification of MRI brain tumors using feature extraction technique," Journal of Medical and Biological Engineering, vol. 40, pp. 307-317, 2020.

[3] E. Dandıl, M. Çakıroğlu, and Z. Ekşi, "Computer-aided diagnosis of malign and benign brain tumors on MR images," in ICT Innovations 2014: World of Data, 2015: Springer, pp. 157-166.

[4] H. Kibriya, M. Masood, M. Nawaz, and T. Nazir, "Multiclass classification of brain tumors using a novel CNN architecture," Multimedia Tools and Applications, vol. 81, no. 21, pp. 29847-29863, 2022.

[5] A. A. Asiri et al., "Brain tumor detection and classification using fine-tuned CNN with ResNet50 and U-Net model: A study on TCGA-LGG and TCIA dataset for MRI applications," Life, vol. 13, no. 7, p. 1449, 2023.

[6] N. A. Samee et al., "Classification framework for medical diagnosis of brain tumor with an effective hybrid transfer learning model," Diagnostics, vol. 12, no. 10, p. 2541, 2022.

[7] S. K. Mathivanan, S. Sonaimuthu, S. Murugesan, H. Rajadurai, B. D. Shivahare, and M. A. Shah, "Employing deep learning and transfer learning for accurate brain tumor detection," Scientific Reports, vol. 14, no. 1, p. 7232, 2024.

[8] "Body MRI." https://www.radiologyinfo.org/en/info/bodymr (accessed.

[9] N. Yamanakkanavar, J. Y. Choi, and B. Lee, "MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer’s disease: a survey," Sensors, vol. 20, no. 11, p. 3243, 2020.

[10] L. Chen, S. Li, Q. Bai, J. Yang, S. Jiang, and Y. Miao, "Review of image classification algorithms based on convolutional neural networks," Remote Sensing, vol. 13, no. 22, p. 4712, 2021.

[11] K. Omer, L. Caucci, and M. Kupinski, "Limitations of CNNs for Approximating the Ideal Observer Despite Quantity of Training Data or Depth of Network," The Journal of imaging science and technology, vol. 64, no. 6, pp. 60408-1, 2020.

[12] A. Mohammed and R. Kora, "A comprehensive review on ensemble deep learning: Opportunities and challenges," Journal of King Saud University-Computer and Information Sciences, vol. 35, no. 2, pp. 757-774, 2023.

[13] B. Naderalvojoud and T. Hernandez-Boussard, "Improving machine learning with ensemble learning on observational healthcare data," in AMIA Annual Symposium Proceedings, 2023, vol. 2023: American Medical Informatics Association, p. 521.

[14] H. H. Sultan, N. M. Salem, and W. Al-Atabany, "Multi-classification of brain tumor images using deep neural network," IEEE access, vol. 7, pp. 69215-69225, 2019.

[15] A. Younis, L. Qiang, C. O. Nyatega, M. J. Adamu, and H. B. Kawuwa, "Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches," Applied Sciences, vol. 12, no. 14, p. 7282, 2022.

[16] M. Rasool et al., "A hybrid deep learning model for brain tumour classification," Entropy, vol. 24, no. 6, p. 799, 2022.

[17] O. Özkaraca et al., "Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images," Life, vol. 13, no. 2, p. 349, 2023.

[18] "Brain Tumor MRI Dataset." https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset (accessed.

[19] M. Bansal, M. Kumar, M. Sachdeva, and A. Mittal, "Transfer learning for image classification using VGG19: Caltech-101 image data set," Journal of ambient intelligence and humanized computing, pp. 1-12, 2023.

[20] T.-H. Nguyen, T.-N. Nguyen, and B.-V. Ngo, "A VGG-19 model with transfer learning and image segmentation for classification of tomato leaf disease," AgriEngineering, vol. 4, no. 4, pp. 871-887, 2022.

[21] B. Han, J. Du, Y. Jia, and H. Zhu, "Zero-watermarking algorithm for medical image based on VGG19 deep convolution neural network," Journal of Healthcare Engineering, vol. 2021, 2021.

[22] "VGG-Net Architecture Explained."https://medium.com/@siddheshb008/vgg-net-architecture-explained-71179310050f (accessed.

[23] W. Li, K. Liu, L. Zhang, and F. Cheng, "Object detection based on an adaptive attention mechanism," Scientific Reports, vol. 10, no. 1, p. 11307, 2020.

[24] H. Wang, F. Zhang, and L. Wang, "Fruit classification model based on improved Darknet53 convolutional neural network," in 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 2020: IEEE, pp. 881-884.

[25] "Darknet53."https://github.com/developer0hye/PyTorch-Darknet53?tab=readme-ov-file (accessed).

[26] T. Magadza and S. Viriri, "Deep learning for brain tumor segmentation: a survey of state-of- the-art," Journal of Imaging, vol. 7, no. 2, p. 19, 2021.

[27] W. Zhao, D. Zhou, X. Qiu, and W. Jiang, “Compare the performance of the models in art classification,” PLOS ONE, vol. 16, no. 3, p. e0248414, Mar. 2021.

[28] B. Naderalvojoud and T. Hernandez-Boussard, “Improving machine learning with ensemble learning on observational healthcare data,” AMIA ... Annual Symposium proceedings. AMIA Symposium, vol. 2023, pp. 521–529, 2024, Accessed: May 03, 2024.

[29] S. Bian and W. Wang, “On diversity and accuracy of homogeneous and heterogeneous ensembles,” International Journal of Hybrid Intelligent Systems, vol. 4, no. 2, pp. 103–128, Jun. 2007.

[30] S. Mascarenhas and M. Agarwal, “A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification,” IEEE Xplore, Nov. 01, 2021.

[31] Akmalbek Bobomirzaevich Abdusalomov, Mukhriddin Mukhiddinov, and Taeg Keun Whangbo, “Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging,” Cancers, vol. 15, no. 16, pp. 4172–4172, Aug. 2023.

[32] N. Habib, Md. M. Hasan, Md. M. Reza, and M. M. Rahman, “Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection,” SN Computer Science, vol. 1, no. 6, Oct. 2020

[33] A. Raza et al., " A hybrid deep learning-based approach for brain tumor classification, " Electronics, vol. 11, no. 7, p. 1146, 2022.

[34] Abida, “A Deep Dive into Pretrained Models: VGG-16, VGG-19, ResNet, AlexNet, and Inception,” Medium, Nov. 04, 2023. https://medium.com/@abidaubaid229/a-deep-dive-into-pretrained-models-vgg-16-vgg-19-resnet-alexnet-and-inception-40df8cfa38c1

[35] M. Bansal, M. Kumar, M. Sachdeva, and A. Mittal, “Transfer learning for image classification using VGG19: Caltech-101 image data set,” Journal of Ambient Intelligence and Humanized Computing, Sep. 2021

[36] L. Yanli, G. Yuan, and Y. Wotao, “An Improved Analysis of Stochastic Gradient Descent with Momentum,” Advances in Neural Information Processing Systems, vol. 33, 2020

[37] M. Al-Jabbar, E. Al-Mansor, S. Abdel-Khalek, and S. Alkhalaf, “Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart cities,” Alexandria Engineering Journal, vol. 75, pp. 29–40, Jul. 2023

[38] K. Raza, “Chapter 8 - Improving the prediction accuracy of heart disease with ensemble learning and majority voting rule,” ScienceDirect, Jan. 01, 2019.

[39] M. A. Ganaie, M. Hu, A. K. Malik, M. Tanveer, and P. N. Suganthan, “Ensemble deep learning: A review,” Engineering Applications of Artificial Intelligence, vol. 115, p. 105151, Oct. 2022.

[40] O. Özkaraca et al., "Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images," Life, vol. 13, no. 2, p. 349, 2023.

[41] F. Uysal and M. Erkan, "Multiclass Classification of Brain Tumors with Various Deep Learning Models," Engineering Proceedings, vol. 27, no. 1, p. 30, 2022.

[42] H. Kibriya, M. Masood, M. Nawaz, and T. Nazir, "Multiclass classification of brain tumors using a novel CNN architecture," Multimedia Tools and Applications, vol. 81, no. 21, pp. 29847-29863, 2022.

[43] M. Rasool et al., "A hybrid deep learning model for brain tumour classification," Entropy, vol. 24, no. 6, p. 799, 2022.

[44] M. A. Gómez-Guzmán et al., “Classifying Brain Tumors on Magnetic Resonance Imaging by Using Convolutional Neural Networks,” Electronics, vol. 12, no. 4, p. 955, Jan. 2023.

[45] A. Priya and V. Vasudevan, “Brain tumor classification and detection via hybrid alexnet-gru based on deep learning,” Biomedical Signal Processing and Control, vol. 89, pp. 105716–105716, Mar. 2024.

[46] G. Boesch, “Ensemble Learning: A Combined Prediction Model (2024 Guide),” viso.ai, Mar. 07, 2024. https://viso.ai/deep-learning/ensemble-learning.

‌[47] Md. Nahiduzzaman et al., “A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images,” Scientific Reports, vol. 15, no. 1, Jan. 2025.

[48] Md. Mamun Hossain, Md. Moazzem Hossain, Most. Binoee Arefin, F. Akhtar, and J. Blake, “Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble,” Diagnostics, vol. 14, no. 1, pp. 89–89, Dec. 2023.

[49] A. Saeed, Khurram Shehzad, S. S. Bhatti, S. Ahmed, and A. T. Azar, “GGLA-NeXtE2NET: A Dual-Branch Ensemble Network with Gated Global-Local Attention for Enhanced Brain Tumor Recognition,” IEEE Access, pp. 1–1, Jan. 2025.

[50] R. Maurya and S. Wadhwani, “An Efficient Method for Brain Image Preprocessing with Anisotropic Diffusion Filter & Tumor Segmentation,” Optik, vol. 265, p. 169474, Sep. 2022.

[51] N. Nobel et al., “A Novel Mixed Convolution Transformer Model for the Fast and Accurate Diagnosis of Glioma Subtypes,” Advanced Intelligent Systems, Nov. 2024.

[52] M. Alsamawi, W. H. Al-Arashi, M. M. Alkhawlani, and F. A. A. J. Almahri, “A Class-Wise Deep Ensemble Framework Using ResNet101 and DenseNet201 for Brain Tumor Classification,” Journal of Imaging Informatics in Medicine, Dec. 2025, doi: https://doi.org/10.1007/s10278-025-01762-6.

التنزيلات

منشور

2026-06-24

إصدار

القسم

Articles

كيفية الاقتباس

[1]
السماوي م., العرشي و. ح., و الخولاني م. م., "تصنيف أورام الدماغ باستخدام التعلم العميق التجميعي المعتمد على تقنية التصويت بالأغلبية", UST J Eng Tech, م 4, عدد 1, ص 101–120, يونيو 2026, doi: 10.59222/ustjet.4.1.4.

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