Browsing by Author "Lallem, Manel"

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    Multi-labeled chest X-Ray images classification using transfer learning
    (Université M’hamed Bougara de Boumerdes : Institut de Genie Electrique et Electronique, 2023) Messouci, Bouchra; Lallem, Manel; Daamouche, Abdelhamid (Supervisor)
    Chest diseases are a prevalent global health issue affecting millions of people worldwide. They can vary in severity with some conditions being relatively mild and others posing serious health risks, if not diagnosed and treated promptly. One of the most common imaging techniques used to diagnose chest pathologies is x-ray .It offers a non-invasive, quick, and relatively cheap mean to gain insights into the internal structures of the chest. However, the interpretation of chest x-ray images can be challenging. Factors such as overlapping structures, variations in image quality, the presence of more than one abnormality in one image, and the need for different viewpoints can complicate the interpretation process. Radiologists and healthcare professionals require specialized training and a high level of expertise to be able to accurately analyze these images and identify potential chest diseases. In this study, we investigate the effectiveness of deep learning methods in detecting pathologies present in chest radiographs. Specifically, we focus on the application of convolutional neural networks for classifying different types of pathologies. Convolutional neural networks have gained popularity due to their capability to learn meaningful image representations at various levels. Our research explores the potential of using networks trained on a non-medical dataset for the multi- labeled classificatio no fvariou sches tpathologies .T oevaluat eth eperformanc eo four algorithms, we used a subset of the CheXpert public data-set available on Kaggle. We also investigated the effect of class balancing using various techniques on the overall performance. Among the different approaches we tested, EfficientNet-B2trained on the balanced data-set yielded the best results. For the various types of pathologies, we achieved an area under the curve (AUC) ranging from 0.850 to 0.876 These results demonstrate the feasibility of utilizing transfer learning approaches to detect pathology in chest X-rays.