Wan Zuki Azman Wan Muhamad
Thumbnail Image
Preferred name
Wan Zuki Azman Wan Muhamad
Official Name
Wan Zuki Azman, Wan Muhamad
Alternative Name
Azman, Wan Zuki
Wan Ahmad, Wan Zuki Azman
Muhamad, Wan Zuki Azmanwan
Main Affiliation
Universiti Malaysia Perlis
Scopus Author ID
55860800560
Researcher ID
R-4128-2019

Research Output

No data found

Filters

1
3
Reset filters

Settings
Now showing 1 - 3 of 3
No Thumbnail Available
Publication

Breast cancer status, grading system, etiology, and challenges in Asia: an updated review

2023 , Tan Xiao Jian , Cheor Wai Loon , Cheng Ee Meng , Khairul Shakir Ab Rahman , Wan Zuki Azman Wan Muhamad , Leow Wai Zhe

The number of breast cancer incidences reported worldwide has increased tremendously over the years. Scoping down to Asia, in 2020, the reported incidences of breast cancer are appalling, comprising 1,026,171 cases, occupying up to 45.4% of cases across the globe. Breast cancer is a non-communicable disease, that emerges in variegated forms, self-subsistent, and the etiology is observed to be multifactorial, dependent on the individual reproductive pattern, hormonal factors, diet, physical activity, lifestyle, and exposure to certain advent procedures. Given this complexity, breast cancer is expected to undergo a persistent increment in the number of incidences in near future, exacerbating the public health quality, regardless of race, ethnicity, geographical subgroups, and socioeconomic. In this review article, the authors examine breast cancer in multiple facets, comprising the updated statistics on breast cancer, typically in Asia; etiology of breast cancer; diagnosis of breast cancer; grading system; and challenges in breast cancer from the country’s income perspective. Realizing the ever-increasing demand for quality treatment, here, the article also contemplates common therapies in breast cancer, such as breast-conserving therapy, mastectomy, postmastectomy radiation therapy, neoadjuvant chemotherapy, axillary surgery, chemotherapy, adjuvant medical therapies, biological and targeted therapies, and endocrine therapy. This review article intended to provide a brief yet broad panoramic view of breast cancer, to readers, ranging from newcomers, existing researchers, and relevant stakeholders in the topic of interest.

View
No Thumbnail Available
Publication

Hybrid Mahalanobis Taguchi System with Binary Whale Optimisation Feature Selection for the Wisconsin Breast Cancer Dataset

2023 , Chow Yong Huan , Wan Zuki Azman Wan Muhamad , Zainor Ridzuan Yahya , Nor Hizamiyani Abdul Azziz , Tan Chye Lih , Tan Xiao Jian

The Mahalanobis-Taguchi System (MTS) is a statistical approach used in breast cancer research to facilitate early detection and promote efficient treatment. The technique analyses mammogram images for significant features using a multivariate statistical analysis technique. It combines the Mahalanobis distance (MD) and Taguchi's method to determine the differences between benign and malignant samples. While orthogonal array (OA) has been widely used in MTS, it has been criticised for providing suboptimal results due to insufficient coverage of feature combinations during the feature optimisation process. To address this issue, the Binary Whale Optimisation Algorithm (BWOA) is proposed as an improved search algorithm for MTS. This paper aims to develop a novel hybrid method that enhances the efficiency of the Mahalanobis Taguchi System (MTS). The performance of feature selection ability due to different MTS hybrid algorithms were also compared. BWOA simulates the hunting behaviour of humpback whales and works by exploring new regions of the solution space, gradually narrowing the search space, and fine-tuning the solution. MTS-BWOA demonstrated its enhanced capability in feature optimisation compared to traditional MTS methods and has the potential to be applied in other medical imaging domains.

View
No Thumbnail Available
Publication

A comprehensive review of tubule formation in histopathology images: advancement in tubule and tumor detection techniques

2024-09-11 , Joseph Jiun Wen Siet , Xiao Jian Tan , Wai Loon Cheor , Khairul Shakir Ab Rahman , Cheng Ee Meng , Wan Zuki Azman Wan Muhamad , Sook Yee Yip

Breast cancer, the earliest documented cancer in history, stands as a foremost cause of mortality, accounting for 684,996 deaths globally in 2020 (15.5% of all female cancer cases). Irrespective of socioeconomic factors, geographic locations, race, or ethnicity, breast cancer ranks as the most frequently diagnosed cancer in women. The standard grading for breast cancer utilizes the Nottingham Histopathology Grading (NHG) system, which considers three crucial features: mitotic counts, nuclear pleomorphism, and tubule formation. Comprehensive reviews on features, for example, mitotic count and nuclear pleomorphism have been available thus far. Nevertheless, a thorough investigation specifically focusing on tubule formation aligned with the NHG system is currently lacking. Motivated by this gap, the present study aims to unravel tubule formation in histopathology images via a comprehensive review of detection approaches involving tubule and tumor features. Without temporal constraints, a structured methodology is established in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, resulting in 12 articles for tubule detection and 67 included articles for tumor detection. Despite the primary focus on breast cancer, the structured search string extends beyond this domain to encompass any cancer type utilizing histopathology images as input, focusing on tubule and tumor detection. This broadened scope is essential. Insights from approaches in tubule and tumor detection for various cancers can be assimilated, integrated, and contributed to an enhanced understanding of tubule formation in breast histopathology images. This study compiles evidence-based analyses into a cohesive document, offering comprehensive information to a diverse audience, including newcomers, experienced researchers, and stakeholders interested in the subject matter.

View