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  • Open Access

    ARTICLE

    Hybrid MNLTP Texture Descriptor and PDCNN-Based OCT Image Classification for Retinal Disease Detection

    Jahida Subhedar1,2, Anurag Mahajan1,*, Shabana Urooj3, Neeraj Kumar Shukla4,5

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2831-2847, 2025, DOI:10.32604/cmc.2025.059350 - 17 February 2025

    Abstract Retinal Optical Coherence Tomography (OCT) images, a non-invasive imaging technique, have become a standard retinal disease detection tool. Due to disease, there are morphological and textural changes in the layers of the retina. Classifying OCT images is challenging, as the morphological manifestations of different diseases may be similar. The OCT images capture the reflectivity characteristics of the retinal tissues. Retinal diseases change the reflectivity property of retinal tissues, resulting in texture variations in OCT images. We propose a hybrid approach to OCT image classification in which the Convolution Neural Network (CNN) model is trained using… More >

  • Open Access

    ARTICLE

    Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction

    M. Vinoth Kumar1,*, M. Prakash2, M. Naresh Kumar3, H. Abdul Shabeer4

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1277-1293, 2023, DOI:10.32604/iasc.2023.025591 - 19 July 2022

    Abstract The most noteworthy neurodegenerative disorder nationwide is apparently the Alzheimer's disease (AD) which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy, a sensitive method for evaluating the AD has to be developed yet. Due to the correlations between ocular and brain tissue, the eye (retinal blood vessels) has been investigated for predicting the AD. Hence, en enhanced method named Enhanced Long Short Term Memory (E-LSTM) has been proposed in this work which aims at finding the severity of AD from ocular biomarkers. To find the… More >

  • Open Access

    ABSTRACT

    Predicting Plaque Progression Using Patient-Specific Fluid-Structure-Interaction Models Based on IVUS and OCT Images with Follow-Up

    Xiaoya Guo1, Dalin Tang1,2,*, David Molony3, Chun Yang2, Habib Samady3, Jie Zheng4, Gary S. Mintz5, Akiko Maehara5, Jian Zhu6, Genshan Ma6, Mitsuaki Matsumura5, Don P. Giddens3,7

    Molecular & Cellular Biomechanics, Vol.16, Suppl.1, pp. 75-76, 2019, DOI:10.32604/mcb.2019.05743

    Abstract Atherosclerotic plaque progression is generally considered to be closely associated with morphological and mechanical factors. Plaque morphological information on intravascular ultrasound (IVUS) and optical coherence tomography (OCT) images could complement each other and provide for more accurate plaque morphology. Fluid-structure interaction (FSI) models combining IVUS and OCT were constructed to obtain accurate plaque stress/strain and flow shear stress data for analysis. Accuracy and completeness of imaging and advanced modeling lead to accurate plaque progression predictions.
    In vivo IVUS and OCT coronary plaque data at baseline and follow-up were acquired from left circumflex coronary and right coronary… More >

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