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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Morphological characteristics of X-ray thorax images of COVID-19 patients using the Bradley thresholding segmentation Retno Supriyanti; Muhammad Alqaaf; Yogi Ramadhani; Haris B. Widodo
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1074-1083

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has made test screening much needed. Currently, the most commonly used is the swab type. Although in fact, there is also a screening method with chest radiology. The purpose of this study is to develop a COVID-19 early detection system based on X-ray images of the patient's thorax in the form of a computer-aided diagnosis. This case is based on the fact that X-ray modalities are available in several health care centers in Indonesia, compared to other modalities such as computed tomography (CT) scan or magnetic resonance imaging (MRI). In this paper, we emphasize the X-ray thorax image segmentation process to explore the morphological information of the thorax. We use the Bradley thresholding segmentation method. The results obtained are promising to be further developed with a performance percentage of 73.33% for the thorax for COVID-19 patients and 54% for the thorax for normal patients.
Morphological features of lung white spots based on the Otsu and Phansalkar thresholding method Retno Supriyanti; Syadzwina Luke Dzihniza; Muhammad Alqaaf; Muhammad Rifqi Kurniawan; Yogi Ramadhani; Haris Budi Widodo
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp530-539

Abstract

COVID-19 is a disease that causes respiratory system disorders, so various tests are needed. One of them uses a chest X-ray or thorax. A chest X-ray will depict the lungs as a whole so that patches like white shadows will be visible. In this study, the number of lung areas and white spots can be observed and detected using segmentation techniques in image processing. But before entering the segmentation stage, the image will go through the preprocessing stage using the tri-threshold fuzzy intensification operators (fuzzy IO) method. It then segmented the lungs using the Otsu method by changing the digital image from grey to black and white based on comparing the threshold value with the pixel colour value of the digital image. Then, further segmentation was carried out using the Phansalkar method to detect and simultaneously count the number of white spots. Referring to the experiments we have carried out, Otsu Phansalkar's segmentation performance promises to be developed further.
Mobile application for diagnosing alzheimer's based on clinical dementia rating Supriyanti, Retno; Putra Yubiksana, Muhammad; Mahardika Wijonarko, Bintang Abelian; Ramadhani, Yogi; Syaiful Aliim, Muhammad; Irham Akbar, Mohammad; Budi Widodo, Haris; Widanarto, Wahyu; Alqaaf, Muhammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1607-1617

Abstract

Alzheimer's is a neurodegenerative disease characterized by memory loss, impaired thinking abilities, and changes in behavior. It is the most common form of dementia, significantly affecting a person's ability to carry out daily activities. Statistics indicate that the number of individuals suffering from Alzheimer's worldwide continues to rise as the population ages. Diagnosing Alzheimer's is a complex process that typically requires a skilled medical team. One diagnostic tool that can be utilized is an MRI machine. Previous research focused on extracting features from MRI images taken from three different cross-sections: axial, coronal, and sagittal. Based on these three types of cross-sectional images, we developed a system to classify the severity of Alzheimer's. This paper focuses on creating an Alzheimer's classification system accessible through a mobile application. The results indicate that our system has a performance accuracy of 90% in classifying the severity of the disease.