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KHACHNAOUI HAJER
KHACHNAOUI HAJER
Institut Superieur des Technologies medicales de Tunis
Verified email at istmt.utm.tn
Title
Cited by
Cited by
Year
Thyroid nodules classification and diagnosis in ultrasound images using fine‐tuning deep convolutional neural network
O Moussa, H Khachnaoui, R Guetari, N Khlifa
International Journal of Imaging Systems and Technology 30 (1), 185-195, 2020
502020
Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review
H Khachnaoui, R Mabrouk, N Khlifa
IET Image Processing 14 (16), 4013-4026, 2020
282020
A review on deep learning in thyroid ultrasound computer-assisted diagnosis systems
H Khachnaoui, R Guetari, N Khlifa
2018 IEEE International Conference on Image Processing, Applications and …, 2018
242018
Machine Learning for Early Parkinson’s Disease Identification within SWEDD Group Using Clinical and DaTSCAN SPECT Imaging Features
H Khachnaoui, N Khlifa, R Mabrouk
Journal of Imaging 8 (4), 97, 2022
192022
Bilinear Pooling for Thyroid Nodule Classification in Ultrasound Imaging
N Aboudi, H Khachnaoui, O Moussa, N Khlifa
Arabian Journal for Science and Engineering, 1-11, 2023
72023
Deep Learning for Automatic Pulmonary Embolism Identification Using CTA Images
H Khachnaoui, M Agrébi, S Halouani, N Khlifa
2022 6th International Conference on Advanced Technologies for Signal and …, 2022
72022
Enhanced Parkinson’s Disease Diagnosis through Convolutional Neural Network Models Applied to SPECT DaTSCAN Images
H Khachnaoui, B Chikhaoui, N Khlifa, R Mabrouk
IEEE Access, 2023
12023
Prediction of Postoperative Visual Acuity in Rhegmatogenous Retinal Detachment Using OCT Images
S Hosni, H Khachnaoui, HM Zgolli, S Mabrouk, D Sidibé, H Tabia, ...
IEEE Access 11, 135435-135448, 2023
2023
Statistical analysis of clinical and DaTSCAN SPECT imaging features' in Parkinson's Disease, SWEDD, and Healthy Control subjects
H Khachnaoui, N Khlifa, R Mabrouk
2022 8th International Conference on Control, Decision and Information …, 2022
2022
Classification automatique des nodules thyroïdiens dans les images échographiques par apprentissage profond
H Khachnaoui, O Moussa, R Guetari
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