Deep Learning-Based Pneumonia Detection from Chest X-Rays Using Convolutional Neural Networks

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@article{1719121,
      author = {Pallavi Tukaram Chaudhary, Prof. Nanda Satish Kulkarni},
      title = {Deep Learning-Based Pneumonia Detection from Chest X-Rays Using Convolutional Neural Networks},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {3144-3154},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719121.pdf},
      abstract = {Pneumonia remains a leading cause of morbidity and mortality worldwide, and chest X-ray (CXR) interpretation is the primary diagnostic tool used to detect it, despite being prone to inter-observer variability and human error. This report presents a complete technical analysis of a CNN-based binary pneumonia classification system implemented in TensorFlow/Keras. The system architecture comprises three convolutional blocks (32, 64, and 128 filters) followed by a fully connected classification head with dropout regularization, trained on the publicly available Kaggle Chest X-Ray Pneumonia dataset. The report documents the data preprocessing pipeline, model architecture, training methodology, and a supplementary rule-based infection-area-estimation routine using Otsu-style binary thresholding. Beyond describing the implementation, this report provides a critical, independent evaluation of the code's design choices, identifies methodological limitations against current best practice in the literature (2024-2026), and proposes specific, technically justified improvements — including transfer learning, class-imbalance handling, data augmentation, k-fold cross-validation, and explainable AI integration via Grad-CAM. All analysis, explanation, and critique in this report is independently authored and does not reproduce text from any external source.},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1719121}
  }