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Mohammed A. Fadhel
Researcher at Information Technology University
Publications - 44
Citations - 1832
Mohammed A. Fadhel is an academic researcher from Information Technology University. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 9, co-authored 39 publications receiving 392 citations.
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Journal ArticleDOI
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi,Jinglan Zhang,Amjad J. Humaidi,Ayad Q. Al-Dujaili,Ye Duan,Omran Al-Shamma,José Santamaría,Mohammed A. Fadhel,Muthana Al-Amidie,Laith Farhan +9 more
TL;DR: In this paper, a comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field is provided, and the challenges and suggested solutions to help researchers understand the existing research gaps.
Journal ArticleDOI
Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
Laith Alzubaidi,Mohammed A. Fadhel,Omran Al-Shamma,Jinglan Zhang,José Santamaría,Ye Duan,Sameer Razzaq Oleiwi +6 more
TL;DR: A deep convolutional neural network (DCNN) model that integrates three ideas including traditional and parallel Convolutional layers and residual connections along with global average pooling is designed that can significantly improve the performance considering a reduced number of images in the same domain of the target dataset.
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Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data.
Laith Alzubaidi,Muthana Al-Amidie,Ahmed Al-Asadi,Amjad J. Humaidi,Omran Al-Shamma,Mohammed A. Fadhel,Jinglan Zhang,José Santamaría,Ye Duan +8 more
TL;DR: A novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deepLearning model on the small amount of labeled medical images is proposed.
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Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis
TL;DR: This research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content, which are different in the number of layers and learnable filters.
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Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model
TL;DR: A hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinomas, benign tumor and normal tissue.