Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3839 2018 2018 College of Computer Science and Information Technology, Wasit University, Al-Kut, Wasit, Iraq Riyadh Riyadh Department of Computer Techniques, Imam Kadhum College, Diwaniyah, Iraq; College of Computer Science and Information Technology, University of Al-Qadisiyah, Diwaniyah, Iraq Ali Hakem Alsaeedi College of Engineering and Technologies, Al-Mustaqbal University, Babil, Iraq Hussein Al Al-Khamees College of Computer Science and Information Technology, Wasit University, Al-Kut, Wasit, Iraq Riyadh Rahef Nuiaa Al Ogaili Information Technology Research and Development Center (ITRDC), University of Kufa, Najaf, Iraq; College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq Zaid Abdi Alkareem Alyasseri Department of Artificial Intelligence, College of Computer Science and Information Technology, University of Anbar, Anbar, 31001, Iraq Mazin Abed Mohammed 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 2025 141 154 10.54216/FPA.200111 https://www.americaspg.com/articleinfo/3/show/3839