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The role of chunking in drawing Rey complex figure

Obaidellah UH, Cheng PC

Percept Mot Skills, 2015 Apr;120(2):535-55.
PMID: 25706345 DOI: 10.2466/24.PMS.120v17x6

The study investigated the effects of chunking and perceptual patterns that guide the drawings of Rey complex figure. Ten adult participants (M age=22.2 yr., SD=4.1) reproduced a single stimulus in four drawing modes including delayed recall, tracing, copying, and immediate recall across 10 sessions producing a total of 400 trials. It was hypothesized that the effect of chunking is most obvious in the free recall tasks than in the tracing or copying tasks. Measures such as pauses, patterns of drawings, and transitions among patterns of drawings suggested that participants used chunking to aid rapid learning of the diagram. The analysis of the participants' sequence of chunk production further revealed that they used a spatial schema to organize the chunks. Findings from this study provide additional evidence to support prior studies that claim graphical information is hierarchically organized.
Integrating color histogram analysis and convolutional neural networks for skin lesion classification

Rasel MA, Kareem SA, Obaidellah U

Comput Biol Med, 2024 Dec;183:109250.
PMID: 39395346 DOI: 10.1016/j.compbiomed.2024.109250

The color of skin lesions is a crucial diagnostic feature for identifying malignant melanoma and other skin diseases. Typical colors associated with melanocytic lesions include tan, brown, black, red, white, and blue-gray. This study introduces a novel feature: the number of colors present in lesions, which can indicate the severity of skin diseases and help distinguish melanomas from benign lesions. We propose a color histogram analysis, a traditional image processing technique, to analyze the pixels of skin lesions from three publicly available datasets: PH2, ISIC2016, and Med-Node, which include dermoscopic and non-dermoscopic images. While the PH2 dataset contains ground truth about skin lesion colors, the ISIC2016 and Med-Node datasets lack such annotations; our algorithm establishes this ground truth using the color histogram analysis based on the PH2 dataset. We then design and train a 19-layer Convolutional Neural Network (CNN) with different skip connections of residual blocks to classify lesions into three categories based on the number of colors present. The DeepDream algorithm is utilized to visualize the learned features of different layers, and multiple configurations of the proposed CNN are tested, achieving the highest weighted F1-score of 75.00 % on the test set. LIME is subsequently applied to identify the most important features influencing the model's decision-making. The findings demonstrate that the number of colors in lesions is a significant feature for describing skin conditions. The proposed CNN, particularly with three skip connections, shows strong potential for clinical application in diagnosing melanoma, supporting its use alongside traditional diagnostic methods.
Fulltext Exploring the opinions of secondary school students on the strengths and weaknesses of the school dental service in Selangor, Malaysia: a qualitative study

Ab Mumin N, Yusof ZYM, Marhazlinda J, Obaidellah U

BMC Oral Health, 2021 08 11;21(1):394.
PMID: 34380484 DOI: 10.1186/s12903-021-01741-7

BACKGROUND: The Malaysian School Dental Service (SDS) was introduced to provide systematic and comprehensive dental care to school students. The service encompasses promotive, preventive, and, curative dental care. This study aimed to undertake a process evaluation of the SDS based on the perspectives of government secondary school students in Selangor, Malaysia.
METHODS: The study adopted a qualitative approach to explore the opinions of secondary school students on the SDS implementation in their schools. Data from focus group discussions involving Form Two (14-year-olds) and Form Four (16-year-olds) students from the selected schools were transcribed verbatim and coded using the NVivo software before framework method analysis was conducted.
RESULTS: Among the strengths of the SDS were the convenience for students to undergo annual oral examination and dental treatment without having to visit dental clinics outside the school. The SDS also reduced possible financial burdens resulting from dental treatment costs, especially among students from low-income families. Furthermore, SDS helped to improve oral health awareness. However, the oral health education provided by the SDS personnel was deemed infrequent while the content and method of delivery were perceived to be less interesting. The poor attitude of the SDS personnel was also reported by the students.
CONCLUSION: The SDS provides effective and affordable dental care to secondary school students. However, the oral health promotion and education activities need to be improved to keep up with the evolving needs of the target audience.
Motivators and barriers to oral hygiene self-care among adolescents in Malaysia: A qualitative study

Ab Mumin N, Yusof ZYM, Marhazlinda J, Obaidellah U

Int J Dent Hyg, 2021 Oct 10.
PMID: 34628709 DOI: 10.1111/idh.12556

OBJECTIVE: Having good oral hygiene self-care, especially a regular toothbrushing habit will promote lifelong oral health. Therefore, understanding the factors that influence an adolescent's oral hygiene behaviour is important in developing effective oral health programmes for this age group. This study aimed to explore the motivators and barriers to adolescents' oral hygiene self-care by exploring the perspectives of secondary school students from three government schools in the state of Selangor, Malaysia.
METHODS: Focus group discussions (FGD) were conducted with Form 2 (14-years-old) and Form 4 (16-years-old) students from selected secondary schools in Selangor using a semi-structured topic guide until data saturation was reached. Data were transcribed verbatim and analysed using framework method analysis.
RESULTS: A total of 10 FGDs were conducted involving 77 adolescents. The motivators for good oral hygiene self-care were appearance, fear of oral disease, consequences of oral disease and past toothache experience. The barriers for oral hygiene self-care were poor attitude towards oral care, lack of confidence in toothbrushing skills, snacking habit and the taste of toothpaste.
CONCLUSION: Understanding the motivators and barriers to adolescents' oral hygiene self-care is the first step in designing effective oral health education messages. The findings from this study can be used as a guide for oral health education programmes and development of materials that fulfil the needs of the adolescent population.
Fulltext A novel online multi-task learning for COVID-19 multi-output spatio-temporal prediction

Wu Z, Loo CK, Obaidellah U, Pasupa K

Heliyon, 2023 Aug;9(8):e18771.
PMID: 37636411 DOI: 10.1016/j.heliyon.2023.e18771

In light of the ongoing COVID-19 pandemic, predicting its trend would significantly impact decision-making. However, this is not a straightforward task due to three main difficulties: temporal autocorrelation, spatial dependency, and concept drift caused by virus mutations and lockdown policies. Although machine learning has been extensively used in related work, no previous research has successfully addressed all three challenges simultaneously. To overcome this challenge, we developed a novel online multi-task regression algorithm that incorporates a chain structure to capture spatial dependency, the ADWIN drift detector to adapt to concept drift, and the lag time series feature to capture temporal autocorrelation. We conducted several comparative experiments based on the number of daily confirmed cases in 20 areas in California and affiliated cities. The results from our experiments demonstrate that our proposed model is superior in adapting to concept drift in COVID-19 data and capturing spatial dependencies across various regions. This leads to a significant improvement in prediction accuracy when compared to existing state-of-the-art batch machine learning methods, such as N-Beats, DeepAR, TCN, and LSTM.
Bluish veil detection and lesion classification using custom deep learnable layers with explainable artificial intelligence (XAI)

Rasel MA, Abdul Kareem S, Kwan Z, Yong SS, Obaidellah U

Comput Biol Med, 2024 Aug;178:108758.
PMID: 38905895 DOI: 10.1016/j.compbiomed.2024.108758

Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a critical feature for diagnosing melanoma, yet research into detecting BWV in dermatological images is limited. This study utilizes a non-annotated skin lesion dataset, which is converted into an annotated dataset using a proposed imaging algorithm (color threshold techniques) on lesion patches based on color palettes. A Deep Convolutional Neural Network (DCNN) is designed and trained separately on three individual and combined dermoscopic datasets, using custom layers instead of standard activation function layers. The model is developed to categorize skin lesions based on the presence of BWV. The proposed DCNN demonstrates superior performance compared to the conventional BWV detection models across different datasets. The model achieves a testing accuracy of 85.71 % on the augmented PH2 dataset, 95.00 % on the augmented ISIC archive dataset, 95.05 % on the combined augmented (PH2+ISIC archive) dataset, and 90.00 % on the Derm7pt dataset. An explainable artificial intelligence (XAI) algorithm is subsequently applied to interpret the DCNN's decision-making process about the BWV detection. The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.
Asymmetric lesion detection with geometric patterns and CNN-SVM classification

Rasel MA, Abdul Kareem S, Kwan Z, Faheem NAA, Han WH, Choong RKJ, et al.

Comput Biol Med, 2024 Jul 13;179:108851.
PMID: 39004048 DOI: 10.1016/j.compbiomed.2024.108851

In dermoscopic images, which allow visualization of surface skin structures not visible to the naked eye, lesion shape offers vital insights into skin diseases. In clinically practiced methods, asymmetric lesion shape is one of the criteria for diagnosing Melanoma. Initially, we labeled data for a non-annotated dataset with symmetrical information based on clinical assessments. Subsequently, we propose a supporting technique-a supervised learning image processing algorithm-to analyze the geometrical pattern of lesion shape, aiding non-experts in understanding the criteria of an asymmetric lesion. We then utilize a pre-trained convolutional neural network (CNN) to extract shape, color, and texture features from dermoscopic images for training a multiclass support vector machine (SVM) classifier, outperforming state-of-the-art methods from the literature. In the geometry-based experiment, we achieved a 99.00 % detection rate for dermatological asymmetric lesions. In the CNN-based experiment, the best performance is found 94 % Kappa Score, 95 % Macro F1-score, and 97 % weighted F1-score for classifying lesion shapes (Asymmetric, Half-Symmetric, and Symmetric).