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Your search keyword '"Iskandar, D."' showing total 3 results
Start Over Author "Iskandar, D." Topic magnetic resonance imaging
3 results on '"Iskandar, D."'

1. Recent Advances in Classification of Brain Tumor from MR Images - State of the Art Review from 2017 to 2021.

Author

Latif G, Al Anezi FY, Iskandar DNFA, Bashar A, and Alghazo J

Subjects
Algorithms, Brain diagnostic imaging, Brain pathology, Humans, Machine Learning, Brain Neoplasms diagnostic imaging, Magnetic Resonance Imaging methods
Abstract

Background: The task of identifying a tumor in the brain is a complex problem that requires sophisticated skills and inference mechanisms to accurately locate the tumor region. The complex nature of the brain tissue makes the problem of locating, segmenting, and ultimately classifying Magnetic Resonance (MR) images a complex problem. The aim of this review paper is to consolidate the details of the most relevant and recent approaches proposed in this domain for the binary and multi-class classification of brain tumors using brain MR images., Objective: In this review paper, a detailed summary of the latest techniques used for brain MR image feature extraction and classification is presented. A lot of research papers have been published recently with various techniques proposed for identifying an efficient method for the correct recognition and diagnosis of brain MR images. The review paper allows researchers in the field to familiarize themselves with the latest developments and be able to propose novel techniques that have not yet been explored in this research domain. In addition, the review paper will facilitate researchers who are new to machine learning algorithms for brain tumor recognition to understand the basics of the field and pave the way for them to be able to contribute to this vital field of medical research., Results: In this paper, the review is performed for all recently proposed methods for both feature extraction and classification. It also identifies the combination of feature extraction methods and classification methods that, when combined, would be the most efficient technique for the recognition and diagnosis of brain tumor from MR images. In addition, the paper presents the performance metrics, particularly the recognition accuracy, of selected research published between 2017-2021., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Published
2022
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3. Deep CNN based MR image denoising for tumor segmentation using watershed transform

Author

Jaafar Alghazo, M. Mohsin Butt, Ghazanfar Latif, Khan Adil H, and bin Awang Iskandar D. N.F

Subjects
Environmental Engineering, Watershed, Computer science, Anisotropic diffusion, General Chemical Engineering, Noise reduction, 02 engineering and technology, Convolutional neural network, 030218 nuclear medicine & medical imaging, Image (mathematics), 03 medical and health sciences, 0302 clinical medicine, Sørensen–Dice coefficient, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), medicine, Segmentation, medicine.diagnostic_test, business.industry, General Engineering, Magnetic resonance imaging, Pattern recognition, Hardware and Architecture, 020201 artificial intelligence & image processing, Artificial intelligence, business, Biotechnology
Abstract

Magnetic Resonance Imaging (MRI) is considered one of the most effective imaging techniques used in the medical field for both clinical investigation and diagnosis. This is due to the fact that MRI provides many critical features of the tissue including both physiological and chemical information. Rician noise affects MR images during acquisition thereby reducing the quality of the image and complicating the accurate diagnosis. In this paper, we propose a novel technique for MR image denoising using Deep Convolutional Neural Network (Deep CNN) and anisotropic diffusion (AD) which we will refer to as Deep CNN-AD. Watershed transform is then used to segment the tumorous portion of the denoised image. The proposed method is tested on the BraTS MRI datasets. The proposed denoising method produced better results compared to previous methods. As denoising process affect the segmentation process therefore better denoised images by proposed technique produced more accurate segmentation with an average Specificity of 99.85% and dice coefficient of 90.46% thus indicating better performance of proposed technique.

Published
2018

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