Journal ArticleDOI
Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images
Mazin Abed Mohammed,Mazin Abed Mohammed,Belal Al-Khateeb,Ahmed Noori Rashid,Dheyaa Ahmed Ibrahim,Mohd Khanapi Abd Ghani,Salama A. Mostafa +6 more
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TLDR
An effort to automate characterization of breast cancer from ultrasound images using multi-fractal dimensions and backpropagation neural networks is presented.About:
This article is published in Computers & Electrical Engineering.The article was published on 2018-08-01. It has received 108 citations till now. The article focuses on the topics: Breast ultrasound & Breast cancer classification.read more
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Enabling technologies for fog computing in healthcare IoT systems
Ammar Awad Mutlag,Mohd Khanapi Abd Ghani,N. Arunkumar,Mazin Abed Mohammed,Mazin Abed Mohammed,Othman Mohd +5 more
TL;DR: A systematic literature review of the technologies for fog computing in the healthcare IoT systems field and analyzing the previous is presented, providing motivation, limitations faced by researchers, and suggestions proposed to analysts for improving this essential research field.
Journal ArticleDOI
A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction
Rizgar R. Zebari,Adnan Mohsin Abdulazeez,Diyar Qader Zeebaree,Dilovan Asaad Zebari,Jwan Najeeb Saeed +4 more
TL;DR: This paper offers a comprehensive approach to FS and FE in the scope of dimensionality reduction, which significantly reduced computational time, and selecting the most accurate classifiers.
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Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
TL;DR: This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress and the future trends and challenges in the classification and detection of breast cancer.
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Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods
Mazin Abed Mohammed,Karrar Hameed Abdulkareem,Alaa S. Al-Waisy,Salama A. Mostafa,Shumoos Al-Fahdawi,Ahmed M. Dinar,Wajdi Alhakami,Abdullah Baz,Mohammed Nasser Al-Mhiqani,Hosam Alhakami,Nureize Arbaiy,Mashael S. Maashi,Ammar Awad Mutlag,Begona Garcia-Zapirain,Isabel de la Torre Díez +14 more
TL;DR: The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS.
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Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease
Salama A. Mostafa,Aida Mustapha,Mazin Abed Mohammed,Raed I. Hamed,N. Arunkumar,Mohd Khanapi Abd Ghani,Mustafa Musa Jaber,Shihab Hamad Khaleefah +7 more
TL;DR: Results show that the MFEA of the multi-agent system finds the best set of features and improves the performance of the classifiers’ diagnosis results.
References
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Journal ArticleDOI
An adaptive weighted median filter for speckle suppression in medical ultrasonic images
TL;DR: In this article, the adaptive weighted median filter (AWMF) is proposed for reducing speckle noise in medical ultrasonic images. But it is not suitable for image segmentation.
Journal ArticleDOI
Texture segmentation using fractal dimension
Bidyut B. Chaudhuri,N. Sarkar +1 more
TL;DR: A modified box-counting approach is proposed to estimate the FD, in combination with feature smoothing in order to reduce spurious regions and to segment a scene into the desired number of classes, an unsupervised K-means like clustering approach is used.
Journal ArticleDOI
A fully automatic and robust brain MRI tissue classification method.
TL;DR: A novel, fully automatic, adaptive, robust procedure for brain tissue classification from 3D magnetic resonance head images (MRI) that customizes a training set, by using a 'pruning' strategy, such that the classification is robust against anatomical variability and pathology.
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Screening for diabetic retinopathy using computer based image analysis and statistical classification
Bernhard Mogens Ege,Ole K. Hejlesen,Ole Vilhelm Larsen,Karina Torp Møller,Barry Jennings,David Kerr,David A. Cavan +6 more
TL;DR: The preliminary development of a tool to provide automatic analysis of digital images taken as part of routine monitoring of diabetic retinopathy in a clinic is described.
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Computer-aided diagnosis applied to US of solid breast nodules by using neural networks.
TL;DR: This system differentiated solid breast nodules with relatively high accuracy and helped inexperienced operators to avoid misdiagnoses because the neural network is trainable, it could be optimized if a larger set of tumor images is supplied.