Ahmad Ashraf Abdul Halim
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Preferred name
Ahmad Ashraf Abdul Halim
Official Name
Ahmad Ashraf, Abdul Halim
Alternative Name
Abdul Halim, Ahmad Ashraf
Halim, Ahmad Ashraf Abdul
Halim, A. A.A.
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Scopus Author ID
57200985251
Researcher ID
AGO-8881-2022

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Existing and emerging breast cancer detection technologies and its challenges: A review

2021-11-01 , Ahmad Ashraf Abdul Halim , Allan Melvin Andrew , Mohd Najib Mohd Yasin , Abd Rahman M.A. , Muzammil Jusoh , Vijayasarveswari Veeraperumal , Hasliza A Rahim @ Samsuddin , Illahi U. , Abdul Karim M.K. , Scavino E.

Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.

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Publication

Internet of things technology for greenhouse monitoring and management system based on wireless sensor network

2017 , Ahmad Ashraf Abdul Halim

The rapid development of agrotechnology is playing an important role in the production of greenhouse plantation for cultivating high value fruits, flowers or vegetables. It is imperative to constantly monitor these high value crops optimal requirements at every phase of the plant growth cycle to maintain the best quality production. However, traditional manual inspection, data collection and control method for large-scale greenhouse plantation deemed inefficient with high costs, time consuming and laborious. This project introduces a scheduler to enhance greenhouse management by taking into considerations the different phases of plant growth. The scheduling concept is also a contribution to this research projects implemented and it is believed there is no specific study on scheduling concepts in the automation system according to specific cycles and phases in the crop. Measuring several points in a greenhouse are required to trace down the local climate parameters to ensure the automation system works properly. Cabling would make the measurement system expensive and vulnerable in a large greenhouse plantation. Moreover, the cabled measurement points are complicated and difficult to maintain and relocate once they are installed. Thus, a Wireless Sensor Network (WSN) consisting of small-size wireless sensor nodes based on ZigBee technology is an attractive and cost-efficient option to build the required system. The system is used to sense and monitor the temperature, humidity, light, soil moisture and carbon dioxide which are essential in the photosynthesis process. The scheduler is build using Visual Basic C# to analyse, display and control the actuators in real-time.

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Publication

Existing and emerging breast cancer detection technologies and its challenges: A review

2021-11-01 , Ahmad Ashraf Abdul Halim , Allan Melvin Andrew , Mohd Najib Mohd Yasin , Abd Rahman M.A. , Muzammil Jusoh , Vijayasarveswari Veeraperumal , Hasliza A Rahim @ Samsuddin , Illahi U. , Abdul Karim M.K. , Scavino E.

Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.

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