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?:abstract
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COVID-19 pandemic is increasing in an exponential rate, with restricted accessibility of rapid test kits So, the design and implementation of COVID-19 testing kits remain an open research problem Several findings attained using radio-imaging approaches recommend that the images comprise important data related to coronaviruses The application of recently developed artificial intelligence (AI) techniques, integrated with radiological imaging, is helpful in the precise diagnosis and classification of the disease In this view, the current research paper presents a novel fusion model hand-crafted with deep learning features called FM-HCF-DLF model for diagnosis and classification of COVID-19 The proposed FM-HCF-DLF model comprises three major processes, namely Gaussian filtering-based preprocessing, FM for feature extraction and classification FM model incorporates the fusion of handcrafted features with the help of local binary patterns (LBP) and deep learning (DL) features and it also utilizes convolutional neural network (CNN)-based Inception v3 technique To further improve the performance of Inception v3 model, the learning rate scheduler using Adam optimizer is applied At last, multilayer perceptron (MLP) is employed to carry out the classification process The proposed FM-HCF-DLF model was experimentally validated using chest X-ray dataset The experimental outcomes inferred that the proposed model yielded superior performance with maximum sensitivity of 93 61%, specificity of 94 56%, precision of 94 85%, accuracy of 94 08%, F score of 93 2% and kappa value of 93 5%
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