2022 |
Souid, A.; Sakli, H. Xception-ResNet Autoencoder for Pneumothorax Segmentation Conférence Institute of Electrical and Electronics Engineers Inc., 2022, ISBN: 9781728184425, (cited By 9). Résumé | Liens | BibTeX | Étiquettes: Auto encoders; Chest x-rays; Convolutional neural network; Deep learning; Dice loss; Image segmentation model; Multiple domains; Pneumothorax; Transfer learning; U-net, Convolutional neural networks, Deep neural networks; Diagnosis; Learning systems; Medical imaging; Semantic Segmentation; Semantics; Transfer learning @conference{Souid2022586,Computer vision has made a significant advance in the medical imaging. Pneumothorax is a severe ailment that can be fatal if the patient does not receive proper care. The main way for diagnosing Pneumothorax is by examining the Chest X-ray by a specialist. The urge of experienced radiologists to anticipate whether someone is suffering from pneumothorax or not by examining chest X-rays is indisputable. Such a facility is not available to everyone. Furthermore, in some circumstances, quick diagnosis is required. In this paper We present a deep learning based image segmentation model capable of predicting Pneumothorax cases by localizing it in chest x-ray exam to help the doctor in making this critical choice. Deep Learning has demonstrated its value in multiple domains, outperforming several state-of-the-arts methods. We seek to overcome this challenge by leveraging deep learning capabilities. We used U-Net architecture with Xception as the encoder and ResNet as a decoder. We obtained encouraging findings, and U-Net works exceptionally well in medical imaging. Our work is listed with in as semantic segmentation. With 77.8 (±0.15), our technique obtains a good outcome in terms of Intersection over Union. © 2022 IEEE. |
2020 |
Chairet, Radhia; Salem, Yassine Ben; Aoun, Mohamed Land cover classification of GeoEye image based on Convolutional Neural Networks Conférence 2020, (Cited by: 2). Résumé | Liens | BibTeX | Étiquettes: Classification algorithm, Convolution, Convolutional neural networks, Decision trees, Deep neural networks, Ecosystem management, Environmental projects, Image classification, Land cover classification, Land cover mapping, Mapping, Overall accuracies, Pixel based classifications, Random forest classifier, Remote sensing @conference{Chairet2020458b,Land cover classification is an important topic in remote sensing. It provides useful information for environmental projects and ecosystem management. To succeed the mapping task, interest should be given to select a suitable classification algorithm. The recent advances in machine learning have shown the very great performances of the deep neural networks for many applications including the land cover mapping. The purpose of this work is to exploit the performance of Convolutional Neural Networks CNN to classify a GeoEye satellite image. Two CNN models with three and four convolution layers are tested to show the impact of the number of layers on the accuracy result. The two architectures outperform, with an overall accuracy more than 81%, the pixel based classification using Random Forest classifier. © 2020 IEEE. |
Publications
2022 |
Xception-ResNet Autoencoder for Pneumothorax Segmentation Conférence Institute of Electrical and Electronics Engineers Inc., 2022, ISBN: 9781728184425, (cited By 9). |
2020 |
Land cover classification of GeoEye image based on Convolutional Neural Networks Conférence 2020, (Cited by: 2). |