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  1. Speckle Noise Diffusion in Knee Articular Cartilage Ultrasound Images
    Shoaib MA, Hossain MB, Hum YC, Chuah JH, Mohd Salim MI, Lai KW
    Curr Med Imaging, 2020;16(6):739-751.
    PMID: 32723246 DOI: 10.2174/1573405615666190903143330
    BACKGROUND: Ultrasound (US) imaging can be a convenient and reliable substitute for magnetic resonance imaging in the investigation or screening of articular cartilage injury. However, US images suffer from two main impediments, i.e., low contrast ratio and presence of speckle noise.

    AIMS: A variation of anisotropic diffusion is proposed that can reduce speckle noise without compromising the image quality of the edges and other important details.

    METHODS: For this technique, four gradient thresholds were adopted instead of one. A new diffusivity function that preserves the edge of the resultant image is also proposed. To automatically terminate the iterative procedures, the Mean Absolute Error as its stopping criterion was implemented.

    RESULTS: Numerical results obtained by simulations unanimously indicate that the proposed method outperforms conventional speckle reduction techniques. Nevertheless, this preliminary study has been conducted based on a small number of asymptomatic subjects.

    CONCLUSION: Future work must investigate the feasibility of this method in a large cohort and its clinical validity through testing subjects with a symptomatic cartilage injury.

  2. Fulltext An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19
    Serena Low WC, Chuah JH, Tee CATH, Anis S, Shoaib MA, Faisal A, et al.
    Comput Math Methods Med, 2021;2021:5528144.
    PMID: 34194535 DOI: 10.1155/2021/5528144
    Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.
  3. Fulltext An Overview of Deep Learning Methods for Left Ventricle Segmentation
    Shoaib MA, Chuah JH, Ali R, Hasikin K, Khalil A, Hum YC, et al.
    Comput Intell Neurosci, 2023;2023:4208231.
    PMID: 36756163 DOI: 10.1155/2023/4208231
    Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning architectures, specifically convolutional neural networks have profoundly become the primary choice for the assessment of cardiac medical images. The left ventricle is a vital part of the cardiovascular system where the boundary and size perform a significant role in the evaluation of cardiac function. Due to automatic segmentation and good promising results, the left ventricle segmentation using deep learning has attracted a lot of attention. This article presents a critical review of deep learning methods used for the left ventricle segmentation from frequently used imaging modalities including magnetic resonance images, ultrasound, and computer tomography. This study also demonstrates the details of the network architecture, software, and hardware used for training along with publicly available cardiac image datasets and self-prepared dataset details incorporated. The summary of the evaluation matrices with results used by different researchers is also presented in this study. Finally, all this information is summarized and comprehended in order to assist the readers to understand the motivation and methodology of various deep learning models, as well as exploring potential solutions to future challenges in LV segmentation.
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