Siti Nurul Aqmariah Mohd Kanafiah
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Siti Nurul Aqmariah Mohd Kanafiah
Official Name
Siti Nurul Aqmariah, Mohd Kanafiah
Alternative Name
Kanafiah, S. N.Aqmariah
Kanafiah, S. N.A.M.
Kanafiah, Siti Nurul Aqmariah Mohd
Aqmariah Kanafiah, S. N.
Kanafiah, S. N. A. M
Main Affiliation
Scopus Author ID
55987982900
Researcher ID
HTR-1815-2023

Research Output

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  • Publication
    Color contrast enhancement on pap smear images using statistical analysis
    ( 2021-01-01)
    Nahrawi N.
    ;
    Wan Azani Wan Mustafa
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Mashor M.Y.
    In the conventional cervix cancer diagnosis, the Pap smear sample images are taken by using a microscope,causing the cells to be hazy and afflicted by unwanted noise. The captured microscopic images of Pap smear may suffer from some defects such as blurring or low contrasts. These problems can hide and obscure the important cervical cell morphologies, leading to the risk of false diagnosis. The quality and contrast of the Pap smear images are the primary keys that could affect the diagnosis’ accuracy. The paper's main objective is to propose the best contrast enhancement to eliminate contrast problems in images and cor-rect them in color images to ensure smooth segmentation. In this paper, the med-ian and standard deviation are used for the image's global and local data where the problem region is normalized by using a special proposed formula. The expected resulting image shows only the object (nuclei and cytoplasm), and a background without any noise. The results were compared with CLAHE, HE, and Gray World, and the performance was evaluated based on PSNR, RMSE, and MAE. Proposed method shows higher PSNR and RMSE value while lower value for MAE compared to other methods. This paper's main impact will help doctors in identifying the patient's disease, such as cervical cancer, based on a Pap smear analysis, and increase the accuracy percentages as compared to the conventional method.
  • Publication
    Comparison between Support Vector Machine and K-Nearest Neighbor Algorithms for Leukemia Images Classification Using Shape Features
    ( 2021-01-01)
    Jusman Y.
    ;
    Hasanah A.N.
    ;
    Purwanto K.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Riyadi S.
    ;
    Hassan R.
    ;
    Mohamed Z.
    Leukemia occurs when the body produces abnormal white blood cells in amounts exceeding the normal limit, making them misfunctioning. It is highly influential on the human immune system. Currently, medical personnel require a long time to recognize leukemia, and it is difficult to distinguish between acute leukemia cells and normal cells. Hence, this study aims to build a system program using white blood cell images with image processing using feature extraction with the Hu moments invariant and the Support Machine Machine (SVM) and K-Nearest Neighbor (K-NN) classification methods. The samples used were 800 blood images divided into two classes, acute and normal, with each class consisting of 400 sample images. Based on the test results from comparing the average value of accuracy and training time in both methods, the highest accuracy value was in the SVM method, with an accuracy of 87.97% and the K-NN method of 83.96%. The fastest training time was in the K-NN method of 2.43 seconds and the SVM method of 3.73 seconds.
  • Publication
    Comparison of Malaria Parasite Image Segmentation Algorithm Using Thresholding and Watershed Method
    ( 2021-02-12)
    Jusman Y.
    ;
    Pusparini A.
    ;
    Nazilah Chamim A.N.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    Malaria is an infectious disease caused by plasmodium that lives and breeds in the red blood cells, transmitted by the Anopheles mosquito. During this time, the paramedics to diagnose symptoms use any imagery that is done manually. In the identification analysis of the malaria parasite cell infection, there is a possibility of human error factor done by paramedics because of the number of samples analyzed. This case is because the human eye tends to be tired while working continuously, leading to misclassification and treatment that is not right. Therefore, it takes a computer-based system that facilitates image processing to paramedics or laboratory technicians to identify the parasite cells and reduce human error instances. This research conducted on identification of the thresholding and watershed of segmentation method for three types of plasmodium parasite, namely Plasmodium falciparum, Plasmodium malaria, and Plasmodium vivax. This study offered modifications thresholding and watershed algorithm. The results showed the success of the technique that can effectively segment on the three types of Plasmodium malaria, which has an accuracy rate above 90% as well as the results of the computation time between the thresholding method could segment imagery for 1-2 seconds and the watershed method intelligent segmented representation for 3-4 seconds.
  • Publication
    Classification of Parasite Malaria in Schizon Stage with GoogleNet and VGG-19 Pre-Trained Models
    ( 2023-01-01)
    Jusman Y.
    ;
    Aftal A.A.
    ;
    Tyassari W.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Hayati N.
    ;
    Mohamed Z.
    The development of artificial intelligence technology has currently given benefit for humans in various fields. In the medical field, artificial intelligence was developed to help medical experts to classify various diseases using medical images, including malaria. Early detection of malaria parasites is important to save the patients, thus this study developed a detection system for some malaria parasites (P. falciparum, P. vivax, and P. malariae) in the schizont stage. This system uses deep learning methods using GoogleNet and VGG 19 pre-trained models. This study performs accuracy, running time, and analysis based on the confusion matrix for testing result. The best training result is performed by the GoogleNet pre-trained model, with an average running time of 7 minutes 14 seconds and an average accuracy of 98.53% \pm 1.27\%. The best model for classifying malaria image in the blood is the GoogleNet model with an accuracy value of 97.41%, precision 100%, recall 93.75%, specificity 100% and f-score 99.53%.
  • Publication
    Development of life cycle classification system for Plasmodium knowlesi malaria species using deep learning
    (AIP Publishing, 2023)
    Muhd Syamir Azhar
    ;
    Mohd Yusoff Mashor
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Zeehaida Mohamed
    In this paper, the performance of deep learning model for GoogleNet and AlexNet are analysed to classify plasmodium knowlesi life cycle stages. Plasmodium knowlesi images are taken from department of microbiology and parasitology in Hospital Universiti Sains Malaysia (HUSM) in this research work. The data images are enhanced using contrast stretching method. The enhanced image undergoes process of segmentation to extract parasite inside the effected red blood cells. The segmented images go through bounding box process according to their size input image for both deep learning models. There are 5940 data which it represents for four classes: artifact, trophozoite, schizont and gametocyte stage. These datasets are trained using GoogleNet and AlexNet to classify the life cycle stages of plasmodium knowlesi. The analysed performance of both models includes training, validation, and testing process. According to the result, both model able to reach 100% for training accuracy. For validation accuracy, AlexNet has higher accuracy with 93.4% compared to GoogleNet with 92.2%. For testing accuracy, Google has higher accuracy with 91.1% where AlexNet with 89.1%.
  • Publication
    Clasification of Malaria images in thropozoid stages using deep learning models
    (IEEE, 2024-03)
    Wikan Tyassari
    ;
    Yessi Jusman
    ;
    Novian Dwi Payana
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Zeehaida Mohamed
    The risk of malaria infection is very high, especially for people living in eastern Indonesia, such as Papua, Maluku, and Nusa Tenggara. In Indonesia there are several types of malaria parasite infected, Plasmodium Falciparum, Plasmodium Vivax, and Plasmodium Malaria. Identifying malaria at an early stage is an important to reduce the risk of death and find suitable treatment. However, identifying and diagnosing malaria is time consuming. Therefore, it is necessary to apply technology in detecting the class of malaria parasites. This study classified images of malaria parasites Plasmodium Falciparum, Plasmodium Vivax, and Plasmodium Malarie at the trophozoite stage using the deep learning pre-trained models AlexNet and Inception-V3. According to accuracy of training, Inception-V3 is the best deep learning model. The performance analysis result of inception is accuracy 98.98% ± 0.71%, precision 98.83% ± 1.44%, recall 98.83% ± 1.38%, specificity 99.11% ± 1.09%, and F-score 98.82% ± 0.83%. However, despite having lower accuracy and performance AlexNet have faster in computational training time.
  • Publication
    Shape Recognition of GPR Images using Hough Transform and PCA plus LDA
    ( 2022-01-01)
    Hasimah Ali
    ;
    Norfatihah Bahari
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Amran T.S.T.
    ;
    Jusman Y.
    Ground penetrating radar (GPR) is a nondestructive test used for shallow subsurface investigation such as land mine detection, mapping and locating buried utilities. In practical applications, GPR images could be noisy due to system noise, the heterogeneity of the medium, and mutual wave interactions. Hence, it is a complex task to recognize the hyperbolic pattern from GPR B-scan images. Thus, this project proposes combined shape recognition of buried objects using Hough Transform (HT) and PCA plus LDA in GPR images. The use of HT is justified because it has the property of transforming global curve detection into efficient peak detection in the Hough parameter space. Whereas PCA plus LDA tries to maximize between-class scatter while minimizing within-class scatter. In this framework, the preprocessed GPR images were extracted using HT. The extracted HT features were subjected to PCA plus LDA to map them from high into lower dimensional features. Then, the reduced PCA+LDA features were used as input to the k-NN classifier to recognize four geometrical shapes cubic, disc, and spherical of the buried objects. Based on the results obtained, the average recognition rate of reduced HT features using PCA plus LDA was achieved 85.30% thus shows a promising result.
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  • Publication
    IoT Based Smart Betta Fish Monitoring system with fish fatality prediction.
    ( 2023-01-01)
    Julida N.L.
    ;
    Siti Marhainis Othman
    ;
    Mohd Nasir Ayob
    ;
    Norasmadi Abdul Rahim
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Muhamad Safwan Muhamad Azmi
    ;
    Mohd Sani Mohamad Hashim
    ;
    Masturah Tunnur Mohamad Talib
    ;
    Nurul Syahirah Khalid
    This study enlightens the importance of rearing water quality to Betta fish health. A water quality monitoring system was developed based on water quality parameters namely water pH, temperature (°C) and TDS level (ppm). Fuzzy Logic Algorithm was applied to predict the possibility of the fish to get infected by the disease using combination of the water quality parameters value. Graphical User Interface (GUI) was developed to test the efficiency of the fish disease prediction system using fuzzy logic algorithm before the fuzzy rule been embedded to the IOT system. Arduino Uno Wi-Fi R2.0 and Blynk Apps used for enabling the system to update the aquarium water quality to owner in real-time. Hydroponic technology implemented in this project for recirculate rearing water inside the fish tank. Theoretically, the aquaponic system will help regulate the water tank parameters in optimum range and Betta Splendens should be free from all diseases.
      5  28
  • Publication
    A novel nucleus detection on pap smear image using mathematical morphology approach
    ( 2021-01-01)
    Nahrawi N.
    ;
    Wan Azani Wan Mustafa
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mohamad Nur Khairul Hafizi Rohani
    ;
    Hasliza A Rahim @ Samsuddin
    The fourth most common form of cancer among women is cervical cancer with 569, 847 new cases and 311, 365 reported deaths worldwide in 2018. Cervical cancer is classified as the third leading cause of cancer among women in Malaysia, with approximately 1, 682 new cervical cases and about 944 deaths occurred in 2018. Cervical cancer can be detected early by cervical cancer screening. Papanicolaou test, also known as Pap smear test is conducted to detect cancer or pre-cancer in the cervix. The disadvantage of this conventional method is that the sample of microscopic images will risk blurring effects, noise, shadow, lighting and artefact problems. The diagnostic microscopic observation performed by a microbiologist is normally time-consuming and may produce inaccurate results even by experienced hands. Thus, correct diagnosis information is essential to assist physicians to analyze the condition of the patients. In this study, an automated segmentation system is proposed to be used as it is more accurate and faster compared to the conventional technique. Using the proposed method in this paper, the image was enhanced by applying a median filter and Partial Contrast Stretching. A segmentation method based on mathematical morphology was performed to segment the nucleus in the Pap smear images. Image Quality Assessment (IQA) which measures the accuracy, sensitivity and specificity were used to prove the effectiveness of the proposed method. The results of the numerical simulation indicate that the proposed method shows a higher percentage of accuracy and specificity with 93.66% and 95.54% respectively compared to Otsu, Niblack and Wolf methods. As a conclusion, the percentage of sensitivity is slightly lower, with 89.20% compared to Otsu and Wolf methods. The results presented here may facilitate improvements in the detection performance in comparison to the existing methods.
      2  29
  • Publication
    Feature Extraction Performance to Differentiate Spinal Curvature Types using Gray Level Co-occurrence Matrix Algorithm
    ( 2020-11-24)
    Jusman Y.
    ;
    Lubis J.H.
    ;
    Chamim A.N.N.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    Spinal curvature type can be detected from digital X-ray images. Experts diagnose spinal curvature for a long time to obtain accurate results. This research aims to analyze the use of image processing techniques to extract features in two types of spinal imagery, normal and abnormal (i.e., scoliosis), by applying the Gray Level Co-occurrence Matrix (GLCM) algorithm and Support Vector Machine (SVM) for the classification method. This study used 40 images divided into 4 data sets for analysis. Three distance parameters, 50, 75, and 100 pixels, and three parameters of quantization values, 8, 16, and 32, were utilized for analysis. The highest accuracy obtained from one of the specific data set was 100%, while the highest accuracy of the average of each value distance and quantization was 90%. The GLCM algorithm could differentiate the abnormality of spinal imagery.
      2  9