Deep CNN-Driven Framework for Automated Detection of COVID-19 from Chest X-Ray Images
Keywords:
supervised, confusion matrix, CNN, python, reinforcedAbstract
The rapid and accurate detection of COVID-19 remains crucial for effective patient management and controlling the spread of the disease. This study presents a Deep Convolutional Neural Network (CNN)–driven framework for the automated detection of COVID-19 from chest X-ray (CXR) images. The proposed framework leverages deep learning to extract complex spatial features and distinguish COVID-19 infections from normal and other pneumonia cases with high precision. A carefully curated dataset comprising publicly available CXR images was preprocessed using normalization, image augmentation, and contrast enhancement techniques to improve feature representation and model robustness. The CNN architecture was optimized through hyperparameter tuning and fine-tuning using transfer learning from pretrained models such as VGG19 and ResNet50. Experimental results demonstrate that the proposed framework achieves superior performance compared to traditional machine learning and baseline deep learning models, with an overall accuracy 88%. Furthermore, Grad-CAM visualization was applied to interpret the model’s decisions, ensuring transparency and clinical reliability. The findings confirm that the proposed deep CNN-based system can serve as a fast, reliable, and cost-effective diagnostic tool to assist radiologists in early COVID-19 detection, especially in regions with limited testing resources.Because it comes with a wide variety of libraries and header files, the Anaconda (Jupyter) notebook is the greatest tool for implementing Python programming.
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