Wan Azani Wan Mustafa
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Wan Azani Wan Mustafa
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Wan Azani, Wan Mustafa
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Mustafa, W.
Azani Mustafa, Wan
Mustaffa, Wan Azani
Wan Mustafa, Wan Azani
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Scopus Author ID
57219421621
Researcher ID
J-4603-2014

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Cervical Cancer Detection Techniques: A Chronological Review

2023-05-01 , Wan Azani Wan Mustafa , Ismail S. , Mokhtar F.S. , Alquran H. , Al-Issa Y.

Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included “(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)”. Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease’s burden on women worldwide.

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A Study of Coding Learning Amongst Children: Motivation and Learning Performance

2023-01-01 , Hanafi H.F. , Wan Azani Wan Mustafa , Idris M.N. , Ghani M.M. , Alkhayyat A. , Lah N.H.C. , Seng W.Y.

Computer programming and coding now face several obstacles in aiding students to improve their grasp of programming and coding. Furthermore, current programming approaches may more effectively measure children's programming aptitudes and abilities, necessitating a reassessment of programming training difficulties. Such a novel technique may compel educators to teach coding more effectively by crystallising multiple children's cognitive backgrounds. Considering this, the authors performed a comprehensive analysis of the existing literature (2022-2023) to identify critical mental elements and motives that might aid in gaining a broad understanding of coding learning. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used to identify and choose relevant publications from three major internet databases: Scopus, Web of Science, and Eric. Initially, 2250 papers were reviewed and retrieved. However, this number was reduced to just 20 based on selection criteria. Several learning outcomes (assessments) and motivational elements (applications and tools) have substantially influenced children's coding and programming learning. According to the final discussion, children are motivated when exposed to pleasant and pleasurable coding environments.

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Performance Analysis of Z-Blade Reaction Type Turbine for Low-Head Low Flowrate Pico Hydro

2021-01-01 , Basar M.F. , Hassan F.S.M. , Rais N.A. , Zulkarnain I.A. , Wan Azani Wan Mustafa

The study explores the performance characteristics of a Z-Blade reaction type water turbine and investigates a test unit for an ideal and practical case using the governing equations derived from the principles of conservation of mass, momentum, and energy. Various analyses are conducted with consideration of the ideal and possible operating condition for low-head (3 m to 5 m) and low flowrate (2.5 L/sec and below) water resources. The relationship of the fluid flow friction known as k-factor with mass flow rate and angular velocity for a Z-Blade turbine model is discussed. The measured performance of two PVC pipe sizes (0.5 inch and 1 inch) of a Z-Blade turbine is presented and evaluated against theoretical results. This work also describes the simple concept of a Z-Blade turbine for a pico-hydro application. A large variation in k-factor with a 1% difference in rotational speed and mass flow rate is presented. The coefficient k-factor is also demonstrated as a strong parameter influencing the mass flow rate and rotational speed performance. This coefficient also has a significant impact on the conversion of potential energy into power output.

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Effect of different filtering techniques on medical and document image

2021-01-01 , Wan Azani Wan Mustafa , Sam S. , Mohd Aminudin Jamlos , Wan Khairunizam Wan Ahmad

Image enhancement is very important stages used in image processing. A normal image enhancement process is using the filtering technique. Filtering helps the problems of the image display and can improvise the quality of the image. The problems that always happened in the image is illumination, noise and under-light images. In addition, these problems also caused a few troubles for image recognition for the daily life of certain people for their work. The objective of this study is to explore and compare a few starts of art filtering techniques based on the mathematical algorithm of the filters and then identifying the best method of the filters. There were a few methods that were selected in this project such as a high pass filter, low pass filter, high boost filter and others. All the selected filter experimented on the medical images and document images. The resulting images were evaluated using the Image Quality Assessments (IQA) which is a global contrast factor (GCF) and signal to noise ratio (SNR). Based on the numerical result, homomorphic low pas filter (HLF) provides a better performance among the other filters in terms of GCF (2.066) and SNR (8.907) value of the selected images.

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SOSFloodFinder: A text-based priority classification system for enhanced decision-making in optimizing emergency flood response

2024-01-01 , Kamal S.H. , Aziz A.A. , Wan Azani Wan Mustafa

Flooding is a significant concern in nations with frequent precipitation because it can instantly affect multiple regions simultaneously. Due to the unpredictability of their occurrence caused by rapid water level rise, it is challenging to predict such natural disasters accurately. During flooding, prompt rescue efforts are crucial for the affected population. Due to flooded highways and residences, rescue teams may have difficulty locating victims. This hinders the potentially perilous and time-consuming rescue operation. To address this problem, we propose a web-based system that integrates natural language processing (NLP) with global positioning system (GPS) functionality. The SOSFloodFinder system provides automatic classification priorities for text messages sent by flood victims, as well as their most recent or current locations. The classification of text based on priority enables efficient resource allocation during rescue operations. In conclusion, this system has the potential to reduce future flood-related fatalities. Additional research and development are necessary to thoroughly investigate this method’s practical capabilities and effectiveness.

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Counting Non-Overlapping Abnormal Cervical Cells in Whole Slide Images

2023-01-01 , Badarneh A. , Alzuet A. , Wan Azani Wan Mustafa , Alquran H. , Alsalatie M. , Mohammed F.F. , Alkhayyat A.

Cervical cancer is one of the most common cancer among women globally. The Pap smear test has been widely used to detect cervical cancers according to the morphological characteristics of the cell nuclei on the micrograph. The aim of this paper is to count the non-overlapping abnormal cervical cells in whole slide images automatically by employing various image techniques. The proposed approach consists of four main steps; image enhancement, transform the extended minima, remove small pixels, and count the number of abnormal cells in the image. The proposed system used 250 cervical pap smear images where the overlap between cells is minimal. The performance of the proposed system is evaluated based on comparing the manual counting and automating counting over whole images. Therefore, the accuracy is evaluated mainly on the difference between manual and automated, and it is 92.5%. The proposed method can be used in laboratory to decrease the false positive rates in counting abnormal cells.

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Pap Smear Image Analysis Based on Nucleus Segmentation and Deep Learning – A Recent Review

2023-02-01 , Alias N.A. , Wan Azani Wan Mustafa , Mohd Aminudin Jamlos , Ismail S. , Alquran H. , Mohamad Nur Khairul Hafizi Rohani

Cervical cancer refers to a dangerous and common illness that impacts women worldwide. Moreover, this cancer affects over 300,000 people each year, with one woman diagnosed every minute. It affects over 0.5 million women annually, leading to over 0.3 million deaths. Recently, considerable literature has grown around developing technologies to detect cervical cancer cells in women. Previously, a cervical cancer diagnosis was made manually, which may result in a false positive or negative. Automated detection of cervical cancer and analysis method of the Papanicolaou (Pap) smear images are still debated among researchers. Thus, this paper reviewed several studies related to the detection method of Pap smear images focusing on Nuclei Segmentation and Deep Learning (DL) from the publication year of 2020, 2021, and 2022. Training, validation, and testing stages have all been the subject of study. However, there are still inadequacies in the current methodologies that have caused limitations to the proposed approaches by researchers. This study may inspire other researchers to view the proposed methods' potential and provide a decent foundation for developing and implementing new solutions.

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Deep CNN-LSTM Network Integration for COVID-19 Classification

2023-01-01 , Shaari F.N. , Abdul Nasir A.S. , Herng O.W. , Wan Azani Wan Mustafa

The COVID-19 virus outbreak has exceeded our expectations and shattered all previous records for virus outbreaks. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes progressive respiratory failure and severe alveolar damage might be deadly. During the pandemic, to curb the virus' spread and ease the strain on the healthcare systems, these has arisen an imperative for swift and precise detection of COVID-19 through computer-aided diagnosis. This paper aims to study the effects of the original and five image enhancement techniques which are Modified Global Contrast stretching (MGCS), Adaptive Gamma Correction with Weighting Distribution (AGCWD), Lowlight (LL), Multi-scale Retinex 2 (MSR2), and Contrast Enhancement using Heat Conduction Matrix (CEHCM) on chest X-ray (CXR) images on the classification process. As a matter of fact, to attain accurate and quick COVID-19 detection, a standard convolutional neural network (CNN) and long short-term memory (LSTM) were developed. A total of 15000 CXR images consisting of COVID-19, normal, and pneumonia were collected from various data repositories to implement this study. The experimental result shows the best classification performance of the CNN-LSTM model is achieved when the system is fed with CXR images enhanced by the lowlight (LL) image enhancement technique, which achieves accuracy, sensitivity, specificity, precision, and F1-score of 99.65%, 99.80%, 99.95%, 99.90%, and 99.85%, respectively.

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Variant Histogram Equalization based Enhancement to Transfer Learning in Detection of COVID-19 Chest X-Ray Images

2023-01-01 , Shaari F.N. , Abdul Nasir A.S. , Wan Azani Wan Mustafa

COVID-19 is a new pulmonary disease that has been straining the global healthcare system because of its high occurrence. It has been found that early-stage COVID-19 can be diagnosed using chest X-ay (CXR) images. Till now, most of the research has concentrated solely on the application of deep learning algorithms, which are valuable but lack proper pre-processing of CXR images. In this context, the purpose of this work is to study the cumulative effects of enhancement approaches on the performance of deep learning models. Within this research, four distinct iterations of histogram equalization image enhancement techniques were utilized on the chest X-ray (CXR) images. These encompass Median-Mean based Sub-Image Histogram Equalization (MMSICHE), Exposure based Sub-Image Histogram Equalization (ESIHE), Dominant Orientation based Texture Histogram Equalization (DOTHE), and Edge-based Texture Histogram Equalization (ETHE). The improved images are subsequently input into two pre-trained neural networks from the Visual Geometry Group (VGG) family, namely the VGG-16 and VGG-19 models, for the purposes of categorizing the CXR images into three categories: COVID-19, normal, and pneumonia. Ultimately, it was observed that the VGG-16 model employing the ESIHE image enhancement technique yielded the highest accuracy, reaching 92.17%.

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Location Technique based on Multiple Partial Discharge Signal in 11kV Underground Power Cable using EMTP-ATP Software

2021-03-09 , Halim M.I.A.M. , Mohamad Nur Khairul Hafizi Rohani , Rosle N. , Rosmi A.S.C. , Yii C. , Wan Azani Wan Mustafa

Power cables are very critical in electrical power systems as power cables failure can interrupt the electrical flow due to unexpected power failure. There are a few sorts of partial discharge (PD) estimations gadgets in the market. For instance, PD can be distinguished by utilizing Rogowski coil (RC) sensors in the disconnected procedure. The current issue PD signal does not usually occur as a single source. Thus, the analysis of multiple PD sources is required to ensure that the cable insulation is in a healthy condition. PD location technique based on multiple signals in 11kV underground power cable was conducted in this research to estimate the accurate location of the PD signal. Modelling of single power cable in a distance of 10km with the RC sensor is installed at several distances to capture the PD signal that travels along the power cable. By selecting the distance between six RC sensors and synchronous multiple PD signal, the design of the power system has been constructed by using EMTP-ATP software. Multi-point technique based on time difference of arrival (TDOA) was performed in the single line power cable to obtain the PD location. The measurement using multi-point of RC sensor technique is preferred based on the conditions due to the value of velocity elimination. Based on the results, the accurate location of PD Source 1 is detected 501 m along RC sensor A1 to RC sensor A3. In contrast, PD source 2 has been detected 2800.15 m along RC sensor A4 to RC sensor A6 with the percentage error of 0.2% and 0.0053%, respectively. The findings show that the location of multiple PD signal that occurred along the line cable can be detected accurately by using the multi-point technique and TDOA. Hence, the performance of the power system has been improved.

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