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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Robotics and Control Systems
Abdullah, Abdul Rahim
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Segmentation and classification techniques used to detect early stroke diagnosis using brain magnetic resonance imaging: a review Kandaya, Shaarmila; Abdullah, Abdul Rahim; Saad, Norhashimah Mohd; Muda, Ahmad Sobri; Ahmad Sabri, Muhammad Izzat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp648-657

Abstract

Stroke is a leading cause of disability and death worldwide. Early diagnosis and treatment are crucial in reducing the risk of stroke-related complications. Brain magnetic resonance imaging (MRI) is a common diagnostic tool used for stroke evaluation. However, manual interpretation of MRI images can be time-consuming and subjective. Machine learning (ML) algorithms have shown promise in automating and improving stroke diagnosis accuracy. This article focuses on classification and segmentation techniques used to detect early stroke diagnosis using brain magnetic imaging. The diagnosis, treatment, and prognosis of complications and patient outcomes in a number of neurological diseases are currently made possible by ML through pattern recognition algorithms. However, the use of MRI is limited because of MRI plays an important role in diagnosing lumbar disc disease. However, the use of MRI is limited due to its high cost and significant operational and processing time. More importantly, MRI is contraindicated in some patients who are claustrophobic or have pacemakers due to the potential for damage. Recent studies have shown that treatment within six hours of a stroke can save a patient's life. Unfortunately, Malaysia is facing a shortage of neuroradiologists, hampering efforts to treat its growing number of stroke patients.
Comparative Analysis of 1D – CNN, GRU, and LSTM for Classifying Step Duration in Elderly and Adolescents Using Computer Vision Lee, Teng Hong; Shair, Ezreen Farina; Abdullah, Abdul Rahim; Rahman, Kazi Ashikur; Ali, Nursabillilah Mohd; Saharuddin, Nur Zawani; Nazmi, Nurhazimah
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1588

Abstract

Developing a classification system that can predict the onset of neurodegenerative diseases or gait-related disorders in elders is vital for preventing incidents like falls. Early detection allows reduction in symptoms and treatment cost for the elderly. In this study, step duration data from five healthy adolescents with age range of 23 – 29 years old and five healthy elderly individuals with age range of 71 – 77 years old were sourced from PhysioNet. To ensure proper training of the deep learning models, synthetic data was generated from the original dataset using a noise jittering technique with random noise of a range between -0.01 and 0.01 added to the original data. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and 1D Convolutional Neural Network (1D-CNN) are used for training the data since the data is available in the form time series data. LSTM and GRU are advanced forms of Recurrent Neural Network (RNN) while 1D – CNN can capture temporal dependencies in sequential data. 1D – CNN has the advantages over GRU and LSTM of being more robust to noise and can capture complex patterns behind the data. These methods will be compared in terms of processing time and accuracy. Results show that 1D – CNN outperforms both LSTM and GRU with accuracy of 1.000 in less than 60 seconds. The novelty and contribution of this research shows that healthy old people and healthy young people can be classified with deep learning. Further direction of the research can explore the deep learning in classification of Parkinson’s disease.
Classification of brain stroke based on susceptibility-weighted imaging using machine learning Kandaya, Shaarmila; Saad, Norhashimah Mohd; Abdullah, Abdul Rahim; Shair, Ezreen Farina; Muda, Ahmad Sobri; Sabri, Muhammad Izzat Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1602-1611

Abstract

Magnetic resonance imaging (MRI) is used to identify brain disorders, particularly strokes. Rapid treatment, often referred to as "time is brain," is emphasized in recent studies, stressing the significance of early intervention within six hours of stroke onset to save lives and enhance outcomes. The traditional manual diagnosis of brain strokes by neuroradiologists is both subjective and time-intensive. To tackle this challenge, this study introduces an automated method for classify brain stroke from MRI images based on pre- and post-stroke patients. The technique employs machine learning, with a focus on susceptibility weighted imaging (SWI) sequences, and involves four stages: preprocessing, segmentation, feature extraction, classification and performance evaluation. The paper proposes classification and performance evaluation to determine stroke region according to three types of categories, those are poor improvement, moderate improvement and good improvement stroke patients based on pre and post patients. Then, performance evaluation is verified using accuracy, sensitivity and specificity. Results indicate that the hybrid support vector machine and bagged tree (SVMBT) yields the best performance for stroke lesion classification, achieving the highest accuracy which is 99% and showing significant improvement for stroke patients. In conclusion, the proposed stroke classification technique demonstrates promising potential for brain stroke diagnosis, offering an efficient and automated tool to assist medical professionals in timely and accurate assessments.
Classifying Gait Disorder in Neurodegenerative Disorders Among Older Adults Using Machine Learning Rahman, Kazi Ashikur; Shair, Ezreen Farina; Abdullah, Abdul Rahim; Lee, Teng Hong; Ali, Nursabillilah Mohd; Zakaria, Muhammad Iqbal; Al Betar, Mohammed Azmi
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1722

Abstract

Gait disorders are a significant concern for older adults, particularly those with neurodegenerative diseases such as Parkinson’s disease, Huntington’s disease, and Amyotrophic Lateral Sclerosis. Accurately classifying these conditions using gait data remains a complex challenge, especially in older populations, due to age-related changes in gait patterns, comorbidities, and increased variability in mobility, which can obscure disease-specific characteristics. This study explicitly classifies neurodegenerative diseases in older adults by analysing age-specific gait force data. Continuous Wavelet Transform (CWT) was utilised for advanced feature extraction, capturing both temporal and spectral signal characteristics. Classifiers including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Multilayer Perceptron (MLP) were employed. The results demonstrated that SVM achieved an accuracy of 87.5%, outperforming RF and MLP, which achieved 83.3% and 50.0%, respectively. These findings underscore the importance of using tailored machine learning approaches to improve the diagnosis and management of neurodegenerative diseases in older adults. The potential for real-world application includes integration into clinical settings, enabling early detection and personalized interventions for individuals with gait disorders.
Co-Authors Ahmad Sabri, Muhammad Izzat Ahmad Sobri Muda, Ahmad Sobri Al Betar, Mohammed Azmi Ali, Nursabillilah Mohd Kandaya, Shaarmila Lee, Teng Hong Nazmi, Nurhazimah Nur Zawani Saharuddin Rahman, Kazi Ashikur Saad, Norhashimah Mohd Sabri, Muhammad Izzat Ahmad Shair, Ezreen Farina Zakaria, Muhammad Iqbal