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@article{169586,
        author = {Udit Joshi and Rahul Singh and Manuj and Ritesh Biswas},
        title = {Automated Pneumonia Detection Using Convolutional Neural Networks on Chest X-Ray Images: A Python-Based Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1760-1767},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169586},
        abstract = {Pneumonia is a serious lung infection that con- tributes significantly to illness and death rates across the globe. Early and accurate diagnosis is essential for effective treatment and better patient recovery. This study introduces a Python-based machine learning method for detecting pneumonia using chest X-ray images. Our approach utilizes deep learning, specifically Convolutional Neural Networks (CNNs), to automatically extract features and classify images. The dataset employed in this research consists of publicly available chest X-rays, with labels indicating both normal and pneumonia-infected cases.
To enhance model performance, we applied image preprocessing steps such as resizing, normalization, and data augmentation. A CNN was designed and trained on the processed data using the Keras and Tensor Flow frameworks. The model’s architecture comprises several convolutional layers, pooling layers, and fully connected layers, optimized through the Adam algorithm. Model evaluation was conducted using metrics like accuracy, precision, recall, F1-score, and the Area Under the Curve (AUC) for the Receiver Operating Characteristic (ROC) curve.
Our findings indicate that the model achieved over 90% ac- curacy in detecting pneumonia, demonstrating strong sensitivity and specificity. This system could support radiologists by improving diagnostic speed and accuracy, thereby minimizing errors and enhancing the quality of medical care. Future improvements could include testing with larger datasets and incorporating Explainability features to increase the model’s transparency for healthcare professionals.},
        keywords = {Pneumonia detection, chest X-ray, deep learning, CNN, Python, medical imaging.},
        month = {November},
        }