S
Saeeda Naz
Researcher at Hazara University
Publications - 76
Citations - 2981
Saeeda Naz is an academic researcher from Hazara University. The author has contributed to research in topics: Cursive & Arabic script. The author has an hindex of 22, co-authored 68 publications receiving 1860 citations. Previous affiliations of Saeeda Naz include Government Post Graduate College & King Saud bin Abdulaziz University for Health Sciences.
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.
Journal ArticleDOI
A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning
TL;DR: The proposed framework conducts three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, gliomas, and pituitary and achieves highest accuracy up to 98.69 in terms of classification and detection.
Journal ArticleDOI
The optical character recognition of Urdu-like cursive scripts
Saeeda Naz,Khizar Hayat,Muhammad Imran Razzak,Muhammad Waqas Anwar,Sajjad A. Madani,Samee U. Khan +5 more
TL;DR: The Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts, with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition in OCR.
Journal ArticleDOI
Refining Parkinson’s neurological disorder identification through deep transfer learning
TL;DR: Experimental results reveal that the proposed deep convolutional neural network classifier with transfer learning and data augmentation techniques provides better detection of Parkinson's disease as compared to state-of-the-art work.