[
  {
    "id": "10.1109/ICPCSN68523.2026.11543623",
    "type": "paper-conference",
    "title": "Deep Learning for Medical Image Analysis: CNN-based Pneumonia Detection on Chest X-Rays",
    "author": [
      {
        "family": "Haveri",
        "given": "Katia"
      },
      {
        "family": "Vajjhala",
        "given": "Narasimha Rao"
      }
    ],
    "container-title": "2026 6th International Conference on Pervasive Computing and Social Networking (ICPCSN)",
    "issued": {
      "date-parts": [
        [
          2026
        ]
      ]
    },
    "page": "156-161",
    "publisher": "IEEE",
    "ISBN": "979-8-3315-7236-5",
    "DOI": "10.1109/ICPCSN68523.2026.11543623",
    "URL": "https://www.narasimharao.net/research/cnn-pneumonia-detection-chest-xray-efficientnetv2/",
    "abstract": "Pneumonia remains one of the leading causes of mortality worldwide, particularly among paediatric populations, and timely radiographic diagnosis is essential for effective clinical intervention. The proliferation of pervasive computing and communication technologies has enabled the deployment of deep learning models within distributed healthcare systems, facilitating rapid and automated medical image analysis at scale. This study presents a convolutional neural network framework based on EfficientNetV2B0 with transfer learning for binary pneumonia classification from paediatric chest X-ray images. The model employs a two-phase training strategy comprising frozen-backbone feature extraction followed by full fine-tuning, augmented by deterministic data preprocessing, class-weighted loss computation, and Gradient-weighted Class Activation Mapping for interpretability. The input to the model consists of 224×224 pixel RGB chest radiographs, and the output is a sigmoid-activated probability score indicating the likelihood of pneumonia. Evaluated on the benchmark Kermany dataset of 5,863 radiographs (1,583 normal and 4,280 pneumonia cases across training, validation, and test splits), the proposed framework achieves an area under the receiver operating characteristic curve (AUC) of 0.967, confirmed as statistically significant via DeLong testing (p < 0.001), and an overall accuracy of 0.894, with a pneumonia recall of 0.98. The model further yields an Expected Calibration Error (ECE) of 0.083 and a Brier Score of 0.091. Bootstrapped confidence intervals and calibration analysis confirm the robustness and reliability of the predictions. The results demonstrate that lightweight EfficientNetV2 architectures, when combined with rigorous reproducibility protocols compliant with CLAIM and TRIPOD-AI standards, can deliver clinically meaningful diagnostic performance suitable for integration into pervasive healthcare information and communication technology platforms.",
    "keyword": "deep learning, convolutional neural networks, pneumonia detection, chest X-ray classification, EfficientNetV2, transfer learning, medical image analysis, pervasive computing, chest X-ray, EfficientNetV2B0, paediatric radiology, Grad-CAM, explainable AI, model calibration, reproducibility, healthcare informatics, edge computing",
    "language": "en"
  }
]