Z
Zeina Al Masry
Researcher at Centre national de la recherche scientifique
Publications - 33
Citations - 283
Zeina Al Masry is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Computer science & Prognostics. The author has an hindex of 6, co-authored 24 publications receiving 92 citations. Previous affiliations of Zeina Al Masry include University of Pau and Pays de l'Adour & Franche Comté Électronique Mécanique Thermique et Optique Sciences et Technologies.
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A CNN-based methodology for breast cancer diagnosis using thermal images
TL;DR: In this paper, the authors presented a computer-aided diagnosis system based on convolutional neural networks as an alternative diagnosis methodology for breast cancer diagnosis with thermal images, which showed that lower false-positives and false-negatives classification rates are obtained when data pre-processing and data augmentation techniques are implemented in these thermal images.
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A CNN-based methodology for breast cancer diagnosis using thermal images
TL;DR: It is demonstrated that a CAD system that implements data-augmentation techniques reach identical performance metrics in comparison with a system that uses a bigger database but without data-AUgmentation, and the influence of data pre-processing, data augmentation and database size on several CAD models is studied.
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An online prognostics-based health management strategy for fuel cell hybrid electric vehicles
TL;DR: An online adaptive prognostics-based health management strategy for fuel cell hybrid electric vehicles, which can improve the durability of the fuel cell thanks to online health monitoring and is designed by considering the progNostics uncertainty through a decision fusion method.
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Robustness testing framework for RUL prediction Deep LSTM networks.
TL;DR: A framework for testing robustness of deep Long Short Term Memory (LSTM) architecture for remaining useful life prediction that enables to gain confidence in the trained LSTM model for RUL prediction and ensures better quality is proposed.
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Prediction of Oncotype DX recurrence score using deep multi-layer perceptrons in estrogen receptor-positive, HER2-negative breast cancer
Aline Baltres,Zeina Al Masry,Ryad Zemouri,Séverine Valmary-Degano,Laurent Arnould,Noureddine Zerhouni,Christine Devalland +6 more
TL;DR: A predictive machine learning model is developed that could help to define patient’s RS and is integrated with histopathological data and DMLP results to select tumor for ODX testing, which allows more relevant use of histopathology data, and optimizes and enhances this information.