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One of the most deadly and destructive illnesses affecting individuals today is the corona virus attack, often known as COVID-19. Due to communal transmission, the Corona virus infection has spread across the entire world. The mortality rate of the patient might be decreased by early sickness identification, yet in asymptomatic settings, via correct diagnosis. As a result, it's essential to create an independent recognition system that, by its swift and accurate results, halts the corona virus's spread. Medical imaging techniques including computed tomography (CT) and chest X-ray images are frequently used to diagnose COVID-19 patients and provide an early prognosis. To identify hidden patterns, deep learning techniques from the medical field are applied. In order to make predictions, Convolutional neural networks (CNNs) are used to extract the chest's X-ray properties. Predictions are made in the patient data utilizing pattern generation to enhance the health plan. To improve categorization, the chest X-ray picture attributes are combined with CNN model training. Extended data are considered throughout the model efficiency evaluation’s testing phase. The proposed CNN-based algorithms outperform other state-of-the-art classification methods in terms of categorization and predicting diseases.

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Determination of Covid-19 Disease Using Hybrid CNN Based Classification Using Deep Learning Applications

  • 章节

  • 发表日期：

    2025年

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  • Vijaya Kumar Koppula,
  • Siva Skandha Sanagala,
  • K. L. S. Soujanya,
  • Avala Raji Reddy,
  • Goda Gangaram,
  • G. Vinesh Shankar

摘要

One of the most deadly and destructive illnesses affecting individuals today is the corona virus attack, often known as COVID-19. Due to communal transmission, the Corona virus infection has spread across the entire world. The mortality rate of the patient might be decreased by early sickness identification, yet in asymptomatic settings, via correct diagnosis. As a result, it's essential to create an independent recognition system that, by its swift and accurate results, halts the corona virus's spread. Medical imaging techniques including computed tomography (CT) and chest X-ray images are frequently used to diagnose COVID-19 patients and provide an early prognosis. To identify hidden patterns, deep learning techniques from the medical field are applied. In order to make predictions, Convolutional neural networks (CNNs) are used to extract the chest's X-ray properties. Predictions are made in the patient data utilizing pattern generation to enhance the health plan. To improve categorization, the chest X-ray picture attributes are combined with CNN model training. Extended data are considered throughout the model efficiency evaluation’s testing phase. The proposed CNN-based algorithms outperform other state-of-the-art classification methods in terms of categorization and predicting diseases.