Oung Qi Wei
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Preferred name
Oung Qi Wei
Official Name
Oung, Qi Wei
Alternative Name
Oung, Qi Wei
Oung, Q. W.
Wei, Oung Qi
Main Affiliation
Scopus Author ID
56405592700
Researcher ID
J-3470-2015

Research Output
Now showing 1 - 4 of 4
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Publication

Evaluation of multimodal sensors for classification of parkinson's disease with severity levels

2021 , Oung Qi Wei

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that has affected a large part of the population. Several symptoms of PD include tremor, rigidity, slowness of movements and vocal impairments. Recently, the evolution of numerous tools for monitoring PD has stimulated the interest of researchers, leading to the possibility of utilising wearable technology to assist in monitoring patient with Parkinson (PWP) state and generate valuable clinical feedback. There have been various initiatives from researchers for PD detection, which includes using (1) Non-signal data acquisition protocols i.e. patients’ diaries, self-reports, clinical rating scales and imaging modalities or through (2) Signal analysis techniques i.e. Bioelectrical signals (EMG signals and EEG signals), motion data signals via wearable motion sensors and speech signals via audio sensor. Among all the initiatives, detecting PD progression using wearable motion sensors and audio sensor has raised significant attention by researchers due to its enormous potentials. However, current signal processing and classification algorithms have limited capability and performance especially involving information obtained from wearable motion sensors for classification of PWP with different severity levels. Additionally, even though there were many works for distinguishing PWP and healthy controls via audio sensor, there are limited researches focusing on multiclass classification of PD. Most of the previous works were conducted based on a binary classification, resulting in early PD stage and the advanced ones being treated equally. Therefore, in this research, a recognition system based on wearable motion sensors and audio sensor information as biomarkers for initial PD detection with the ability for multiclass classification was developed. Three classes of PD severity levels were considered – ‘mild’, ‘moderate’ and ‘severe’ from healthy controls to give the classification results more accurate for clinical use. First, the signals from wearable motion sensors and audio sensor were decomposed by the proposed algorithms known as empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT) respectively. Then, several features were extracted from the coefficients of all decomposed signals. The performance of the algorithm was validated using three classifiers – K-nearest neighbour (KNN), probabilistic neural network (PNN) and extreme learning machine (ELM). Validation of the proposed algorithms demonstrated the effectiveness of wavelet energy, entropy-based and Higher Order Spectra (HOS) - based features from the EWT/ EWPT decomposition for multiclass PD severity levels classification with the highest average accuracy of 96.95% from ELM RBF kernel classifier. Finally, a non-invasive screening system using the proposed algorithm for detection of PD severity levels was also developed using Matlab Graphical User Interface (GUI) Toolbox. It is hoped, the GUI system eases medical professionals to monitor the progression of PD.

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The current challenges review of deep learning-based nuclei segmentation of diffuse large b-Cell Lymphoma

2025 , Gei Ki Tang , Lim Chee Chin , Faezahtul Arbaeyah Hussain , Oung Qi Wei , Aidy Irman Yazid , Sumayyah Mohammad Azmi , Yen Fook Chong , Haniza Yazid

Diffuse Large B-Cell Lymphoma stands as the most prevalent form of non-Hodgkin lymphoma worldwide, constituting approximately 30 percent of cases within this diverse group of blood cancers affecting the lymphatic system. This study addresses the challenges associated with the accurate DLBCL segmentation and classification, including difficulties in identifying and diagnosing DLBCL, manpower shortage, and limitations of manual imaging methods. The study highlights the potential of deep learning to effectively segment and classify DLBCL types. The implementation of such technology has the potential to extract and preprocess image patches, identify, and segment the nuclei in DLBCL images, and classify DLBCL severity based on segmented nuclei counting.

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Understanding Domain Knowledge in Initialization Method for K-Mean Clustering Algorithm in Medical Images

2022-01-01 , Tan X.J. , Nazahah Mustafa , Mohd Yusoff Mashor , Ab Rahman K.S. , Wan Zuki Azman Wan Muhamad , Leow Wai Zhe , Cheor Wai Loon , Oung Qi Wei

This work serves as a preliminary study to investigate and identify the applicability of domain knowledge as an initialization method for K-Mean (KM), typically in medical images. For this purpose, 20 breast histopathology images were used as data set and the evaluations are focused on the clustering of the hyperchromatic nucleus. The iteration numbers and clustering results (i.e., accuracy, over-segmentation, and under-segmentation) are benchmarked with KM++ and the conventional random initialization method. The domain knowledge initialization method is found promising by achieving lower iteration numbers (<9), higher percentage in accuracy (85.5% (±2.27)), and lower percentages in over-segmentation (8.25% (±2.23)), and under-segmentation (7.00% (±2.14)). From this study, we hypothesize that the domain knowledge initialization method has the potential to be implemented as an initialization method and is posited to overperform some established initialization methods, typically for clustering tasks in medical images.

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Global, regional, and national mortality of Larynx cancer from 1990 to 2021: results from the global burden of disease study

2025-03 , Deqian Han , Lee Hoi Leong , Oung Qi Wei , Chia Hau Lee

Background: Larynx cancer, a major upper respiratory tract malignancy, remains a global public health challenge, driven by smoking, alcohol use, and chronic inflammation, despite medical and public health advancements. Methods: Data from the Global Burden of Disease 2021 study were used to assess larynx cancer mortality trends from 1990 to 2021 across global, regional, and national levels. Death rates, absolute mortality numbers, and Estimated Annual Percentage Change (EAPC) were calculated. Results: Globally, the number of deaths from larynx cancer increased by 36.67% between 1990 and 2021, while death rates slightly declined, with an EAPC of -0.41. Males consistently accounted for the majority of deaths, with 100,393 deaths in 2021, though female mortality showed a larger percentage increase of 60.13% compared to 33.39% in males. Significant regional disparities were evident, with the highest death rates reported in Eastern Europe and Central Latin America, where countries like Bulgaria and Cuba recorded rates exceeding 6 per 100,000 population. In contrast, Oceania reported the lowest rates, below 0.5 per 100,000. The elderly (75 + years) experienced the largest increase in mortality, rising by 85.4%, while deaths among the 15–49 age group remained relatively stable. Additionally, larynx cancer death rates were correlated with SDI. Conclusion: Despite slight declines in global death rates, the absolute burden of larynx cancer has increased due to population growth and aging. Regional disparities emphasize the need for targeted interventions and improved healthcare access. This study offers valuable insights for policy and resource planning.

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