aspg-3-3839-1791417642 20261008000042 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 20 1 Saif Ali Abd Alradha Alsaidi College of Computer Science and Information Technology, Wasit University, Al-Kut, Wasit, Iraq Ali Hakem Alsaeedi Department of Computer Techniques, Imam Kadhum College, Diwaniyah, Iraq; College of Computer Science and Information Technology, University of Al-Qadisiyah, Diwaniyah, Iraq Hussein Al-Khamees College of Engineering and Technologies, Al-Mustaqbal University, Babil, Iraq Riyadh Rahef Nuiaa Al Ogaili College of Computer Science and Information Technology, Wasit University, Al-Kut, Wasit, Iraq Zaid Abdi Alkareem Alyasseri Information Technology Research and Development Center (ITRDC), University of Kufa, Najaf, Iraq; College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq Mazin Abed Mohammed Department of Artificial Intelligence, College of Computer Science and Information Technology, University of Anbar, Anbar, 31001, Iraq The nature of images can differ in texture, contrast, illumination, noise levels, and structural patterns. The descriptor suitable for one image may not be optimal for another. Therefore, this paper proposes a new hybrid diagnostic model that combines multi-descriptor feature extraction with a Deep Belief Network. It is used to classify Acute Lymphoblastic Leukaemia. The proposed model consists of two phases: feature extraction and classification. Three descriptors, Histogram of Oriented Gradients, Scale-Invariant Feature Transform, and Convolutional Neural Network are employed in the feature extraction phase. Each descriptor captures different aspects of the image using distinct computational techniques. The Deep Belief Network was trained on each group of features individually. Three trained Deep Belief Network were produced with each data extract by descriptors. The membership function between the training set and the test data determines which DBN will be selected. The model was tested and evaluated on the 10,661 Leukaemia images of the C-NMC_Leukaemia dataset. It consists of two classes of images: 7272 images of Leukaemia cancer and 3389 of the Benign. Experimental results showed that the proposed model achieved an accuracy outperforming several recent methods. The accuracy of the proposed model reaches 96.87%, while the best accuracy of the recent works is 94.91%. 2025 141 154 3839 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.200111 https://www.americaspg.com/journal/3/article/3839 https://www.americaspg.com/storage/11746649033.pdf