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Your search keyword '"Ma, Yong-Kui"' showing total 21 results
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21 results on '"Ma, Yong-Kui"'

1. Edge Computing in the Digital Era

Author

Rasheed, Zahid, primary, Ma, Yong-Kui, additional, Ullah, Inam, additional, Tao, Yuning, additional, Khan, Ijaz, additional, Khan, Habib, additional, and Shafiq, Muhammad, additional

Published
2024
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2. Binary Classification of Alzheimer Disease using sMRI Imaging modality and Deep Learning

Author

Tufail, Ahsan Bin, Zhang, Qiu-Na, and Ma, Yong-Kui

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for the development of possible future treatment option(s). Structural magnetic resonance images (sMRI) plays an important role to help in understanding the anatomical changes related to AD especially in its early stages. Conventional methods require the expertise of domain experts and extract hand-picked features such as gray matter substructures and train a classifier to distinguish AD subjects from healthy subjects. Different from these methods, this paper proposes to construct multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. The whole brain image was passed through two transfer learning architectures; Inception version 3 and Xception; as well as custom Convolutional Neural Network (CNN) built with the help of separable convolutional layers which can automatically learn the generic features from imaging data for classification. Our study is conducted using cross-sectional T1-weighted structural MRI brain images from Open Access Series of Imaging Studies (OASIS) database to maintain the size and contrast over different MRI scans. Experimental results show that the transfer learning approaches exceed the performance of non-transfer learning based approaches demonstrating the effectiveness of these approaches for the binary AD classification task.

Published
2018
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3. Integrating Convolutional Neural Networks with Attention Mechanisms for Magnetic Resonance Imaging-Based Classification of Brain Tumors.

Author

Rasheed, Zahid, Ma, Yong-Kui, Ullah, Inam, Al-Khasawneh, Mahmoud, Almutairi, Sulaiman Sulmi, and Abohashrh, Mohammed

Subjects
*MAGNETIC resonance imaging, *IMAGE recognition (Computer vision), *CONVOLUTIONAL neural networks, *MAGNETICS, *BRAIN tumors
Abstract

The application of magnetic resonance imaging (MRI) in the classification of brain tumors is constrained by the complex and time-consuming characteristics of traditional diagnostics procedures, mainly because of the need for a thorough assessment across several regions. Nevertheless, advancements in deep learning (DL) have facilitated the development of an automated system that improves the identification and assessment of medical images, effectively addressing these difficulties. Convolutional neural networks (CNNs) have emerged as steadfast tools for image classification and visual perception. This study introduces an innovative approach that combines CNNs with a hybrid attention mechanism to classify primary brain tumors, including glioma, meningioma, pituitary, and no-tumor cases. The proposed algorithm was rigorously tested with benchmark data from well-documented sources in the literature. It was evaluated alongside established pre-trained models such as Xception, ResNet50V2, Densenet201, ResNet101V2, and DenseNet169. The performance metrics of the proposed method were remarkable, demonstrating classification accuracy of 98.33%, precision and recall of 98.30%, and F1-score of 98.20%. The experimental finding highlights the superior performance of the new approach in identifying the most frequent types of brain tumors. Furthermore, the method shows excellent generalization capabilities, making it an invaluable tool for healthcare in diagnosing brain conditions accurately and efficiently. [ABSTRACT FROM AUTHOR]

Published
2024
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8. On Disharmony in Batch Normalization and Dropout Methods for Early Categorization of Alzheimer’s Disease

Author

Tufail, Ahsan Bin, primary, Ullah, Inam, additional, Rehman, Ateeq Ur, additional, Khan, Rehan Ali, additional, Khan, Muhammad Abbas, additional, Ma, Yong-Kui, additional, Hussain Khokhar, Nadar, additional, Sadiq, Muhammad Tariq, additional, Khan, Rahim, additional, Shafiq, Muhammad, additional, Eldin, Elsayed Tag, additional, and Ghamry, Nivin A., additional

Published
2022
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15. Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples

Author

Tufail, Ahsan Bin, Ullah, Inam, Khan, Wali Ullah, Asif, Muhammad, Ahmad, Ijaz, Ma, Yong-Kui, Khan, Rahim, Ullah, Kalim, Ali, Md. Sadek, Tufail, Ahsan Bin, Ullah, Inam, Khan, Wali Ullah, Asif, Muhammad, Ahmad, Ijaz, Ma, Yong-Kui, Khan, Rahim, Ullah, Kalim, and Ali, Md. Sadek

Abstract

Diabetic retinopathy (DR) is a worldwide problem associated with the human retina. It leads to minor and major blindness and is more prevalent among adults. Automated screening saves time of medical care specialists. In this work, we have used different deep learning (DL) based 3D convolutional neural network (3D-CNN) architectures for binary and multiclass (5 classes) classification of DR. We have considered mild, moderate, no, proliferate, and severe DR categories. We have deployed two artificial data augmentation/enhancement methods: random weak Gaussian blurring and random shift along with their combination to accomplish these tasks in the spatial domain. In the binary classification case, we have found the performance of 3D-CNN architecture trained by deploying combined augmentation methods to be the best, while in the multiclass case, the performance of model trained without augmentation is the best. It is observed that the DL algorithms working with large volumes of data may achieve better performances as compared to the methods working with small volumes of data.

Published
2021

21. An efficient bit loading algorithm for OFDM system.

Author

HUANG Xin-lin, WANG Gang, MA Yong-kui, ZHANG Cheng-wen, and JIANG Hao

Subjects
MECHANICAL loads, STRAINS & stresses (Mechanics), ALGORITHMS, ORTHOGONAL frequency division multiplexing, TELECOMMUNICATION systems, DIGITAL modulation, DIGITAL communications
Abstract

We propose an efficient bit-loading algorithm to minimize the total power to transmit a target rate of a single user in OFDM communication systems. Considering the digital modulations exploited by 802. 11a, which are BPSK, QPSK, 16QAM and 64QAM (constellation using Gray code and each sub-carrier carrying a maximum of 6 bits) , ⌊ RT/6 ⌋ subcarriers which have litter power addition are allocated with 2 bits each time. The proposed bit loading algorithm is optimal and has the same performance as Hughes-Hartogs algorithm, but greatly reduces the computational complexity. Simulation results show that the proposed bit loading algorithm has the same bit allocation results as Hughes-Hartogs algorithm, but the computational complexity is less than 50% compared with that of Hughes-Hartogs algorithm. The computational complexity will be further reduced when the transmission rate or subcarriers number increases. [ABSTRACT FROM AUTHOR]

Published
2010

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