Enhancing COVID-19 detection using CT-scan image analysis and disease classification: the DI-QL approach

Purpose: The COVID-19 pandemic represents a significant global crisis, resulting in a substantial number of fatalities worldwide and placing immense strain on healthcare systems. Swift and accurate disease detection is crucial for timely treatment and the prevention of complications in such infectious disease. Methods: Various traditional techniques, including data mining, machine learning (ML), and deep learning (DL), have been employed for COVID-19 identification and classification. However, these methods often exhibit limitations in terms of precision and computational complexity. To address these challenges, the proposed system leverages Deep Intensive Quantum Learning (DI-QL) for the classification of COVID-19 and non-COVID diseases. Quantum computing is harnessed to tackle intricate problems, while deep intensive learning facilitates high-speed computation. This system integrates two quantum gates, namely Hadamard gates for data clustering and coupling gates within the quantum circuit to create multiple layers for in-depth classification. Results: The performance of the proposed system is assessed using key performance metrics, such as precision, recall, F1-score, and accuracy. Furthermore, this approach is compared with existing techniques to evaluate the effectiveness of the proposed classification system. Conclusions: The results of the DI-QL approach confirm its exceptional performance and demonstrate its potential as an effective solution for COVID-19 disease classification.