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.
【저자키워드】 COVID-19, deep learning, Medical imaging, feature fusion, firefly algorithm, 【초록키워드】 Diseases, magnetic resonance imaging, database, COVID-19 disease, Computed tomography, X-ray, early diagnosis, Accuracy, outbreak, healthcare, automated, computer vision, COVID-19 patients, diagnose, Combination, Analysis, deaths, Support, COVID-19 case, positive correlation, researcher, Medical doctor, feature, selected, analyzed, collected, caused, carried, healthy, conducted, fused, machine learning classifier, Radiopaedia, Wiener, 【제목키워드】 learning, fusion, optimization, Image, Recognition, deep, Firefly,