Gait Image Classification Using Deep Learning Models for Medical Diagnosis
نویسندگان
چکیده
Gait refers to a person’s particular movements and stance while moving around. Although each gait is unique made up of variety tiny limb orientations body positions, they all have common characteristics that help define normalcy. Swiftly identifying such are difficult spot by the naked eye, can in monitoring elderly who require constant care support. Analyzing silhouettes easiest way assess make any necessary adjustments for smooth gait. It also becomes an important aspect decision-making analyzing progress patient during medical diagnosis. images publicly available Chinese Academy Sciences (CASIA) Database was used this study. After evaluating using CASIA B C datasets, paper proposes Convolutional Neural Network (CNN) CNN Long Short-Term Memory (CNN-LSTM) model classifying silhouette images. Transfer learning models as MobileNetV2, InceptionV3, Visual Geometry Group (VGG) networks VGG16 VGG19, Residual Networks (ResNet) like ResNet9 ResNet50, were compare efficacy proposed models. proved be best achieving highest accuracy 94.29%. This followed CNN-LSTM, which arrived at 93.30% 87.25% accuracy, respectively.
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ژورنال
عنوان ژورنال: Computers, materials & continua
سال: 2023
ISSN: ['1546-2218', '1546-2226']
DOI: https://doi.org/10.32604/cmc.2023.032331