Abstract
Medical images classification and decision making via Internet of Medical Things (IoMT) applications is a challenging task. The qualities of medical images are optimized and lower resolution data is transferred via the communication channel to IoT servers, making the decision support a complicated computing process. In this paper, a novel convolutional neural networking (CNN) model and long short term memory (LSTM) model based decision making of low resolution medical images is computed. The framework is generalized for MRI datasets, the CNN + LSTM combination extracts features from low resolution medical images and further classifies the order of MRI application based on the thresholding feature of host application. The technique is cross-validated with multiple MRI dataset samples for performance estimation. The interim decision making and integrated learning model provides the framework an added efficiency on optimized computing.
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The corresponding author can provide the dataset generated and analyzed during this study upon reasonable request.
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The authors acknowledged the New Horizon College of Engineering, Bengaluru, Visvesvaraya Technological University, Belagavi, India for supporting the research work by providing the facilities.
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R, S., Kapse, A.S. Low Resolution MRI Images Privacy Feature Mapping and Classification Using LSTM-CNN Models in IoMT Healthcare Applications. SN COMPUT. SCI. 5, 880 (2024). https://doi.org/10.1007/s42979-024-03230-4
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DOI: https://doi.org/10.1007/s42979-024-03230-4