Wan Azani Wan Mustafa
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Preferred name
Wan Azani Wan Mustafa
Official Name
Wan Azani, Wan Mustafa
Alternative Name
Mustafa, W.
Azani Mustafa, Wan
Mustaffa, Wan Azani
Wan Mustafa, Wan Azani
Main Affiliation
Scopus Author ID
57219421621
Researcher ID
J-4603-2014

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  • Publication
    Breast Cancer Detection and Classification on Mammogram Images Using Morphological Approach
    ( 2022-01-01)
    Wan Azani Wan Mustafa
    ;
    Azmi A.A.
    ;
    Mohd Aminudin Jamlos
    ;
    Alquran H.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Ismail S.
    ;
    Alkhayyat A.
    ;
    Haron J.
    Breast cancer is one of the most common cancers affecting women worldwide. Mammography is the most well-known and effective method to detect early signs of breast cancer. The purpose of this paper is to detect breast cancer on the mammogram image to classify the disease through morphological techniques. Using conventional methods makes radiology difficult to detect cancer found in the patient's breast. This proposal can be divided into several elements, which are input database, image preprocessing, image segmentation, morphological analysis, and object recognition. First, image preprocessing will be done using the Weiner and Median filters. Second, the thresholding method for image segmentation will be performed, and lastly, morphology will remove imperfections introduced during the image segmentation process. Finally, the image is classified into two classes: normal and cancerous images. A median filter and 0.95 thresholding achieve an accuracy of 93.71%, a sensitivity of 94.36%, and a specificity of 82.53% for the cancerous images.
  • Publication
    H. pylori Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner
    ( 2023-02-01)
    Yacob Y.M.
    ;
    Alquran H.
    ;
    Wan Azani Wan Mustafa
    ;
    Alsalatie M.
    ;
    Sakim H.A.M.
    ;
    Lola M.S.
    Atrophic gastritis (AG) is commonly caused by the infection of the Helicobacter pylori (H. pylori) bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on H. pylori-associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect H. pylori infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.
  • Publication
    Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning
    ( 2022-12-01)
    Qasmieh I.A.
    ;
    Alquran H.
    ;
    Zyout A.
    ;
    Al-Issa Y.
    ;
    Wan Azani Wan Mustafa
    ;
    Alsalatie M.
    A corneal ulcers are one of the most common eye diseases. They come from various infections, such as bacteria, viruses, or parasites. They may lead to ocular morbidity and visual disability. Therefore, early detection can reduce the probability of reaching the visually impaired. One of the most common techniques exploited for corneal ulcer screening is slit-lamp images. This paper proposes two highly accurate automated systems to localize the corneal ulcer region. The designed approaches are image processing techniques with Hough transform and deep learning approaches. The two methods are validated and tested on the publicly available SUSTech-SYSU database. The accuracy is evaluated and compared between both systems. Both systems achieve an accuracy of more than 90%. However, the deep learning approach is more accurate than the traditional image processing techniques. It reaches 98.9% accuracy and Dice similarity 99.3%. However, the first method does not require parameters to optimize an explicit training model. The two approaches can perform well in the medical field. Moreover, the first model has more leverage than the deep learning model because the last one needs a large training dataset to build reliable software in clinics. Both proposed methods help physicians in corneal ulcer level assessment and improve treatment efficiency.
  • Publication
    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.
  • Publication
    Automated Diagnosis of Eye Fundus Images
    ( 2023-01-01)
    Zyout A.
    ;
    Alquran H.
    ;
    Wan Azani Wan Mustafa
    ;
    Alsalatie M.
    ;
    Al-Badarneh A.
    ;
    Khairunizam W.
    Eye disease is a severe health problem. Advanced stages of the disease may lead to vision loss. Early detection may limit the development of the severity and enhance the chance of treatment. Eye disease comes from various factors such as diabetes, increasing pressure in the eye (Glaucoma), and age-related macular degeneration. Ocular fundus 2D images are one of the most common tools used to diagnose the lining of tissue eyes. Huge data availability, increasing cases, and heavy responsibility in the health sector encourage seeking new diagnosis techniques to enhance accuracy and reduce false positive and false negative diagnoses. Computer-aided diagnosis (CAD) is the state-art-technology. This paper proposes a CAD system that combines image processing techniques and artificial intelligence. The proposed method used the green channel of fundus eye images to extract the most representative features by the trained convolutional neural network to classify five eye diseases of fundus images. The build CAD system exploits deep learning and support vector machine classifier to achieve a highly accurate model of 98% for five types of eye diseases.
  • Publication
    Detection of Polycystic Ovary Syndrome (PCOS) Using Machine Learning Algorithms
    ( 2022-01-01)
    Hdaib D.
    ;
    Almajali N.
    ;
    Alquran H.
    ;
    Wan Azani Wan Mustafa
    ;
    Al-Azzawi W.
    ;
    Alkhayyat A.
    One of the most common diseases in women of reproductive age is Polycystic Ovary Syndrome (PCOS). PCOS diagnosis can be tricky, because not everyone with PCOS has polycystic ovaries (PCO), nor does everyone with ovarian cysts have PCOS, hence the pelvic ultrasound as a stand-alone diagnosis is not sufficient. The full diagnostic plan is mainly a combination of a pelvic ultrasound besides blood tests of specific parameters that indicate the presence of PCOS. Since PCOS is a hard-to-diagnose widespread hormonal disorder, blood tests, symptoms, and other parameters with the help of a computer can form a new and easy method to diagnose it. Therefore, we had successfully built a high performing diagnostic model using MATLAB. The data was obtained from the website Kaggle, and the dataset is called Polycystic Ovary Syndrome. In this paper various machine algorithms were employed by utilizing seven classifiers. Results demonstrated that Linear Discriminant classifier exhibits the best performance in terms of accuracy, while in terms of sensitivity, the KNN classifier had the best result. Also, a comparison with four other research papers that exploited the same PCOS dataset was done in terms of implementation platforms, evaluation methods, classifiers, classes, accuracy, and precision of each classifier. Our research excelled among all in terms of accuracy and varied in precedence with precision. MATLAB had shown substantial results and a great model fitting embedded approaches, scoring a high accuracy and precision outcome compared to other studies. Other improvements on the overall PCOS prediction can involve employing preprocessed ultrasound images with the features presented in the dataset.
  • Publication
    EOG Based Eye Movements and Blinks Classification Using Irisgram and CNN-SVM Classifier
    ( 2023-01-01)
    Zyout A.
    ;
    Alquraan O.
    ;
    Alsalatie M.
    ;
    Alquran H.
    ;
    Alqudah A.M.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohammed F.F.
    ;
    Alkhayyat A.
    The classification of eye movements and blinks is an important task in various fields, including ophthalmology, psychology, and human-computer interaction. In recent years, the use of EOG signals and convolutional neural networks (CNNs) has shown promising results in accurately classifying different types of eye movements and blinks. The Irisgram, which is a two-dimensional representation of the short-time Fourier transform in the shape of a human iris, has been used as a feature for distinguishing between different types of eye movements and blinks. Additionally, CNNs have been utilized to learn the features automatically from Irisgrams and classify the eye movements and blinks based on these learned features. In this paper, we provide a methodology to classify blinks and four eye movements by employing Irisgram as input to the CNN-SVM classifier which achieved test accuracy of 96.2% in the testing dataset.
  • Publication
    Classification of Lung Cancer by Using Machine Learning Algorithms
    ( 2022-01-01)
    Al-Tawalbeh J.
    ;
    Alshargawi B.
    ;
    Alquran H.
    ;
    Al-Azzawi W.
    ;
    Wan Azani Wan Mustafa
    ;
    Alkhayyat A.
    Due to the structure of cancer cells, where the majority of the cells are overlapped with each other, early diagnosis of lung cancer is a difficult challenge, so the cause of lung cancer remains unknown and prevention is difficult, early discovery of lung cancer is the only approach to cure it. The classification of lung cancer is a crucial process, based on signs that appear on patients; cancers could easily be predicted and treated. This paper uses KNN, SVM, Naïve Bayes and narrow neural network (NNN) classifiers. KNN showed 85.87% accuracy. SVM, Naïve Bayes and NNN showed 92.6%, 90.3% and 90% accuracy respectively. Results came after the fact that among the four classifiers SVM was the most accurate and much better to classify our data.
  • Publication
    Automated Classification of Skin Lesions Using Different Classifiers
    ( 2023-01-01)
    Al-Tawalbeh J.
    ;
    Alshargawi B.
    ;
    Al-Daraghmeh M.
    ;
    Alquran H.
    ;
    Wan Azani Wan Mustafa
    ;
    Al-Dolaimy F.
    ;
    Alkhayyat A.
    Human skin cancer is the most common death. Skin cancer is defined as the abnormal growth of skin cells that most commonly occurs in areas of the body that are exposed to sunlight, but it can occur anywhere on the body. In their early stages, the majority of skin cancers are curable. As a result, detecting skin cancer early and quickly can save a patient's life. The incidence of malignant melanoma, the most dangerous type of skin cancer, rises year after year. Detecting skin cancer from a skin lesion is difficult due to artifacts, low contrast, mole, scar, etc. Due to the new technological advancements, early detection of skin cancer is now possible. This paper uses K-nearest neigbour (KNN), Artificial neural network (ANN) and support vector machine (SVM) classifiers for segmented and non-segmented groups and shows 95.8% overall accuracy for all classes, with the sensitivity of 97%, 91.4% and 99.7% for Benign, melanoma, seborrheic keratosis, respectively as well a precision of 92.4%, 96.6% and 99.7%, respectively. With all automatically extracted features, the accuracy is better in a non-segmented case. This paper could be extended and further processed to meet an everyday demand of how the lesions are classified or if there are any cancers.
  • Publication
    Pap Smear Images Classification Using Machine Learning: A Literature Matrix
    ( 2022-12-01)
    Alias N.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Alquran H.
    ;
    Hanafi H.F.
    ;
    Ismail S.
    ;
    Rahman K.S.A.
    Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications.