Publications Scientifiques

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Author: search.filters.author.Gaceb, DjamelSubject: search.filters.subject.Deep learning

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    Review on deep learning optimization using knowledge and dataset distillation in medical imaging diagnostics
    (2025) Laribi, Nor-Elhouda; Gaceb, Djamel; Rezoug, Abdellah; Touazi, Faycal
    The integration of deep learning-based artificial intelligence solutions in hospital environments introduces significant challenges, including data privacy restrictions, limited computational resources, and constraints related to the quality and simplicity of the models used. In this review, we highlight the recent advancements in knowledge distillation and dataset distillation as emerging solutions to these challenges in the field of medical imaging. These techniques offer practical benefits in clinical settings by enabling faster training, reduced model size, improved inference speed, and enhanced accuracy, while supporting privacy-preserving learning across decentralized systems and edge devices. Knowledge distillation transfers knowledge from a complex to a simple model, enabling efficient deployment without high loss in diagnostic performance. Dataset distillation, by contrast, focuses on synthesizing datasets that match the pretrained model on real data, reducing data storage requirements. Together, these methods improve learning efficiency, model accuracy, and resource optimization in hospital workflows. However, their integration into medical environments also presents limitations. Challenges such as pipeline complexity, scalability issues, and performance inconsistency across architectures or high-resolution tasks still persist. Overall, this review provides a comprehensive overview of potential and limitations of these two types of distillations in healthcare, offering insights into how these methods can support more scalable, accurate, and privacy-aware AI solutions for medical imaging.
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    Offline Arabic handwritten character recognition: from conventional machine learning system to deep learning approaches
    (2022) Faouci, Soumia; Gaceb, Djamel; Haddad, Mohammed
    Researchers have made great strides in the area of Arabic handwritten character recognition in the last decades especially with the fast development of deep learning algorithms. The characteristics of Arabic manuscript text pose several problems for a recognition system. This paper presents a conventional machine learning system based on the extraction of a set of preselected features and an SVM classifier. In the second part, a simplified convolutional neural network (CNN) model is proposed, which is compared to six other CNN models based on the pre-trained architectures. The suggested methods were tested using three databases: two versions of the OIHACDB dataset and the AIA9K dataset. The experimental results show that the proposed CNN model obtained promising results, as it is able to recognise 94.7%, 98.3%, and 95.6% of the test set of the three databases OIHACDB-28, OIHACDB-40, and AIA9K, respectively.
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    Application of Deep Transfer Learning in Medical Imaging for Thyroid Lesion Diagnostic Assistance
    (Institute of Electrical and Electronics Engineers, 2024) Chaouchi, Lynda; Gaceb, Djamel; Touazi, Fayçal; Djani, Djouher; Yakoub, Assia
    This academic work evaluates and compares the performance of various deep convolutional neural network (DCNN) architectures in classifying thyroid nodules into two categories, malignant and benign, using ultrasound images. The dataset comprises 269 cases of benign lesions and 526 cases of malignant lesions. Given the limited dataset size, we employ a progressive learning approach with three established CNN models: VGG-16, ResNet-50, and EfficientNet. Initially pretrained on ImageNet, these models undergo further fine-tuning using a radiographic image dataset related to a different medical condition but similar to our domain. Different levels and fine-tuning strategies are applied to these models. A supervised softmax classifier is used for classifying lesions as malignant or benign, with the exception of the VGG-16 model. For the VGG-16 model, two additional classifiers, Support Vector Machine (SVM) and Random Forest (RF), are evaluated. The results obtained demonstrate the possibility of easily transitioning from the classification of one disease to another, even with a limited number of images, by leveraging the knowledge already acquired from another extensive database.
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    Super-resolution of document images using transfer deep learning of an ESRGAN model
    (IEEE, 2022) Kezzoula, Zakia; Gaceb, Djamel; Gritli, Nadjat
    This paper presents a novel super-resolution approach of document images. It is based on transfer deep learning of an ESRGAN model. This model, which showed good robustness on natural images, has been adapted to document images by using better levels of fine-tuning and a post-processing to enhance contrast. The experiments were carried out on our document image dataset that we built from document images presenting different challenges. Documents of different categories with different complexity levels and degradation kinds. The results obtained are better compared to ten existing approaches, which we have developed and tested on the same dataset with the same evaluation protocol
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    A review of recent progress in deep learning-based methods for MRI brain tumor segmentation
    (Institute of Electrical and Electronics Engineers Inc, 2020) Chihati, S.; Gaceb, Djamel
    Brain tumor segmentation is a challenging task that involves delimiting cancerous tissues with heterogeneous and diffuse forms in brain medical images. This process is undoubtedly an important step in computer-aided diagnosis systems, in which tumor regions must be isolated for visualization and subsequent analysis. Recently, great progress has been made in brain tumor segmentation with the emergence of deep learning-based methods, which automatically learn hierarchical, and discriminative features from raw data. These methods outperformed the classical machine learning approaches where handcrafted features are used to describe the differences between pathological and healthy tissues. In this paper, we present a comprehensive overview of recent progress in deep learning-based methods for brain tumor segmentation from magnetic resonance images. Moreover, we discuss the most common challenges and suggest possible solutions