Title Covid-19 case recognition from chest ct images by deep learning, entropy-controlled firefly optimization, and parallel feature fusion /
Authors Khan, Muhammad Attique ; Alhaisoni, Majed ; Tariq, Usman ; Hussain, Nazar ; Majid, Abdul ; Damaševičius, Robertas ; Maskeliūnas, Rytis
DOI 10.3390/s21217286
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Is Part of Sensors.. Basel : MDPI. 2021, vol. 21, iss. 21, art. no. 7286, p. 1-19.. ISSN 1424-8220
Keywords [eng] COVID-19 ; Deep learning ; Feature fusion ; Firefly algorithm ; Medical imaging
Abstract [eng] In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach—parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2021
CC license CC license description