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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems
Najjar, Fallah H.
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Journal : International Journal of Robotics and Control Systems

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Hybrid Deep Learning Model for Hippocampal Localization in Alzheimer's Diagnosis Using U-Net and VGG16 Najjar, Fallah H.; Hassan, Nawar Banwan; Kadum, Salman Abd
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1739

Abstract

Alzheimer's disease (AD) is a complex neurodegenerative disease that involves considerable challenges in accurately diagnosing and locating the?affected brain regions. This paper?proposes a new fusion model based on VGG16 and U-Net to achieve accurate segmentation of hippocampus localization and improve AD diagnostic accuracy. Compared to previous techniques such as hierarchical fully?convolutional networks (FCNs) or LBP-TOP localization (an accuracy range of 68% to 95%), our approach achieved a superior accuracy (98.6%) with a mean Jaccard index of 97.3%, like the predicted accuracy range of conventional imaging analysis techniques. By utilizing pre-trained transfer learning models and sophisticated data augmentation methods,?generalization to different datasets greatly reduced over-fitting. Although existing approaches?usually require labor-intensive segmentation or employ handcrafted features, our model automates the hippocampus's localization, leading to improved efficiency and scalability. The effectiveness of our method is strongly supported by the performance metrics including Mean Squared Error (MSE) and Avg. error Standard Deviation which show that MSE values were 5 times lower than those produced using the Hough-CNN based?approach (0.0507 vs. 4.4%). Real-world demands include the need for minimal computational complexity and dependence?on pre-processed ADNI MRI datasets compromising generalizability in actual clinical frameworks. Our results?demonstrated that the fusion model yields superior hippocampal segmentation performance and a new standard for AD diagnostic scores, making a substantial impact on both academic and clinical domains.
A Morphological Context Blocks Hybrid CNN for Efficient Acute Lymphoblastic Leukemia Classification Dubai, Nada Jabbar; Kadhim, Ola Najah; Najjar, Fallah H.
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1824

Abstract

Acute Lymphoblastic Leukemia (ALL) is an aggressive?hematologic malignancy that necessitates early and accurate diagnosis for improved therapeutic efficacy. Although it is a routine practice, the visual blood smear analysis is tedious and?subject to human inaccuracies. This paper proposes a novel morphology-guided deep learning approach called Morphological Context Blocks (MCB)-HyperNet embedding morphological operations into a hybrid CNN architecture. The CNN architectures depend mainly on automatic learning through convolutive filters, so they miss crucial?morphological features that distinguish between leukemic and normal cells. In this study, we propose a deep learning-based approach that directly incorporates morphological dilation?and erosion in the deep learning data pipeline to exploit the potential of morphological feature extraction for our specific task, resulting in enhanced accuracy and reduced diagnostic costs, which ultimately can improve patient outcomes. In addition, the computational efficiency and modularity of the MCB-HyperNet framework facilitate easy adaptation and scalability to many other medical imaging tasks, such as the classification of various diseases, except the classification of?leukemia.  We trained the proposed MCB-HyperNet on different image resolutions from the ALL dataset (168×168, 224×224, 256×256), different batch sizes (16 and 32), and also different training epochs (30, 35, 40, 45, 50) to get the best hyperparameter configuration. The MCB-HyperNet takes advantage of the strong feature extraction ability of ResNet and the light computing resource of MobileNetV3, ultimately obtaining 99.69% accuracy, 98.78% precision, 99.49% sensitivity,?99.12% F1-score, and 99.78% specificity. This new integration greatly enhances the accuracy of early detection, minimizes diagnostic errors, and could have?significant clinical and economic advantages. MCB-HyperNet is a mini CNN, so it shows a good balance between efficiency and accuracy, making scalability and extensibility possible in more medical imaging tasks.
Co-Authors AbdulAmeer, Ansam Ali Dubai, Nada Jabbar Hassan, Nawar Banwan Kadhim, Ola Najah Kadum, Salman Kadum, Salman Abd