Accurate detection of coronavirus cases using deep learning with attention mechanism and genetic algorithm

• Published in: Multimedia tools and applications Vol. 83; no. 34; pp. 81477 - 81490
• Main Author: Kara, Ahmet
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2024; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Communication Networks; Computer Science; Coronaviruses; Data Structures and Information Theory; Deep learning; Effectiveness; Genetic algorithms; Machine learning; Multimedia Information Systems; Special Purpose and Application-Based Systems; State-of-the-art reviews; Track 2: Medical Applications of Multimedia; Viral diseases; Deep learning; Attention mechanism; Detection; Genetic algorithm; Coronavirus disease
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18850-4

The novel coronavirus disease has caused severe threats to the daily life and health of people all over the world. Hence, early detection and timely treatment of this disease are significant to prevent the coronavirus's spread and ensure more effective patient care. This work adopted an integrated framework comprising deep learning and attention mechanism to provide a more effective and reliable diagnosis. This framework consists of two convolution neural network (CNN), a bidirectional LSTM, two fully-connected layers (FCL), and an attention mechanism. The main aim of the proposed framework is to reveal a promising approach based on deep learning for early and timely detection of coronavirus disease. For greater accuracy, the framework's hyperparameters are tuned by means of a genetic algorithm. The effectiveness of the proposed framework has been examined utilizing a public dataset including 18 different blood findings from Albert Einstein Israelita Hospital in Sao Paulo, Brazil. Additionally, within the experimental studies, the proposed framework is subjected to comparison with the state-of-the-art techniques, evaluated across various metrics. Based on the derived consequences, the proposed framework has yielded enhancements in accuracy, recall, precision, and F1-score, registering approximate improvements of 1.27%, 4.07%, 3.20%, and 2.88%, respectively, as measured against the second-best rates.