Publications Scientifiques

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Author: search.filters.author.Gaceb, DjamelSubject: search.filters.subject.Computer vision

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    Progressive Deep Transfer Learning for Accurate Glaucoma Detection in Medical Imaging
    (Institute of Electrical and Electronics Engineers, 2024) Yakoub, Assia; Gaceb, Djamel; Touazi, Fayçal; Bourahla, Nourelhouda
    Glaucoma leads to permanent vision disability by damaging the optic nerve, which transmits visual images to the brain. The fact that glaucoma doesn't exhibit any symptoms as it progresses and can't be halted in later stages makes early diagnosis critical. Although various deep learning models have been applied to detect glaucoma from digital fundus images, the scarcity of labeled data has limited their generalization performance, along with their high computational complexity and specialized hardware requirements. In this study, a progressive transfer learning with preprocessing techniques is proposed for the early detection of glaucoma in fundus images. The performance of this approach is compared against transfer learning and convolutional neural networks using three benchmark datasets: Cataract, Glaucoma and Origa. The experimental results demonstrate that reusing pre-trained models from ImageNet and applying them to a database containing the same disease leads to improved performance, compared to using databases with different diseases in progressive transfer learning. Additionally, applying preprocessing techniques to the databases further enhances the results.
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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.