M
Muhammad Imran Razzak
Researcher at University of Technology, Sydney
Publications - 86
Citations - 3250
Muhammad Imran Razzak is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Cursive & Arabic script. The author has an hindex of 25, co-authored 85 publications receiving 2240 citations. Previous affiliations of Muhammad Imran Razzak include University of Sydney & King Saud University.
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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
Big data analytics for preventive medicine
TL;DR: This review introduces disease prevention and its challenges followed by traditional prevention methodologies, and summarizes state-of-the-art data analytics algorithms used for classification of disease, clustering, anomalies detection, and association as well as their respective advantages, drawbacks and guidelines.
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.