A
Ahmad Zaib
Publications - 6
Citations - 1114
Ahmad Zaib is an academic researcher. The author has contributed to research in topics: Image segmentation & Health care. The author has an hindex of 5, co-authored 6 publications receiving 709 citations.
Papers
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Book ChapterDOI
Deep Learning for Medical Image Processing: Overview, Challenges and the Future
TL;DR: In this paper, the authors discuss state-of-the-art deep learning architecture and its optimization when used for medical image segmentation and classification, and discuss the challenges of deep learning methods with regard to medical imaging and open research issue.
Posted Content
Deep Learning for Medical Image Processing: Overview, Challenges and Future
TL;DR: In this paper, state-of-the-art deep learning architecture and its optimization used for medical image segmentation and classification is discussed. And the challenges deep learning based methods for medical imaging and open research issue are discussed.
Posted ContentDOI
Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning
TL;DR: Pre-trained deep learning models develop in this study could be used early screening of coronavirus, however it calls for extensive need to CT or X-rays dataset to develop a reliable application.
Journal ArticleDOI
Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset
TL;DR: VGG architecture outperforms the state-of-the-art techniques and number of architectures of conveNet in Alzheimer’s disease detection, and achieves an identification test set accuracy of 99.27% (MCI/AD), 98.89% (AD/CN) and 97.06% ( MCI/CN).
Journal ArticleDOI
Extreme learning machine based microscopic red blood cells classification
Syed Hamad Shirazi,Arif Iqbal Umar,Nuhman ul Haq,Saeeda Naz,Muhammad Imran Razzak,Ahmad Zaib +5 more
TL;DR: This work presents a novel method based on extreme machine learning approach for the classification of red blood cells (RBC) images, which has produced more promising results as compared to the existing techniques.