Fusion Model For Covid-19 Diagnosis Using Chest X-Ray Images

Autor: Mohammed Hafeez M. K, M. Sharmila Kumari, Hana Fathima, Haleemathul Shifa Tahseen, Shrama K, Vaishnavi N Shetty
Jazyk: angličtina
Rok vydání: 2023
Předmět:
DOI: 10.5281/zenodo.7638533
Popis: The COVID-19 pandemic increased at an exponential rate and faced a major problem due to the restricted accessibility of rapid test kits. The radio-imaging approach recommends that the images comprise important data related to coronaviruses. The proposed FM-HCF-DLF model is comprised of Gaussian filtering-based pre-processing, a fusion model for feature extraction and classification. The proposed FM model is a combination of handcrafted features with the help of local binary patterns (LBP), deep learning (DL) features and the convolutional neural network (CNN)-based Inception v3 technique using the Adam optimizer. The multilayer perceptron (MLP) is employed to carry out the classification process. The proposed FM-HCF-DLF model used a chest X-ray dataset for experimentation purposes. The experimental outcomes yielded superior performance with a maximum sensitivity of 93.61%, specificity of 94.56%, the precision of 94.85%, the accuracy of 94.08%, F score of 93.2% and kappa value of 93.5%.
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Databáze: OpenAIRE
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