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Titre: | Application of Deep Transfer Learning in Medical Imaging for Thyroid Lesion Diagnostic Assistance |
Auteur(s): | Chaouchi, Lynda Gaceb, Djamel Touazi, Fayçal Djani, Djouher Yakoub, Assia |
Mots-clés: | Thyroid Lesion detection Computer-aided diagnosis system in medical imaging Deep learning Computer vision Artificial intelligence |
Date de publication: | 2024 |
Editeur: | Institute of Electrical and Electronics Engineers |
Collection/Numéro: | 2024 8th International Conference on Image and Signal Processing and their Applications (ISPA), Biskra, Algeria, 2024;pp. 1-7 |
Résumé: | 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. |
URI/URL: | https://ieeexplore.ieee.org/document/10536856 DOI: 10.1109/ISPA59904.2024.10536856 http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/14084 |
Collection(s) : | Communications Internationales
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