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This research paper introduces an integrated framework for pneumonia prediction using X-ray images. The system encompasses a user-friendly flask web application named “Virtual Doctor.” The frontend employs HTML, CSS, and JavaScript to provide an intuitive interface for uploading X-ray images. On the backend, a pre-trained CNN model analyzes images, and predictions are displayed on the user interface. The framework enhances medical diagnosis and showcases the synergy of machine learning, web development, and health care. The research contributes a versatile tool for medical practitioners and patients, merging computer science with health care. The prototype’s accuracy is validated through evaluations on a curated X-ray dataset. The Virtual Doctor concept holds promise for advancing telemedicine and diagnostic solutions, emphasizing timely and accurate healthcare interventions.

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Advancements in Pneumonia Detection from Chest X-ray Images: Deep Learning Solutions for Improved Medical Diagnosis

  • 会议论文

  • 发表日期：

    2025年

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  • Shaik Salma Begum,
  • Adilakshmi Yannam,
  • Modepalli Vignesh,
  • Mandadapu Chiranjeevi Chitrasimha Chowdary,
  • Medarametla Sai Ramya,
  • Yalamanchi Jahnavi

摘要

This research paper introduces an integrated framework for pneumonia prediction using X-ray images. The system encompasses a user-friendly flask web application named “Virtual Doctor.” The frontend employs HTML, CSS, and JavaScript to provide an intuitive interface for uploading X-ray images. On the backend, a pre-trained CNN model analyzes images, and predictions are displayed on the user interface. The framework enhances medical diagnosis and showcases the synergy of machine learning, web development, and health care. The research contributes a versatile tool for medical practitioners and patients, merging computer science with health care. The prototype’s accuracy is validated through evaluations on a curated X-ray dataset. The Virtual Doctor concept holds promise for advancing telemedicine and diagnostic solutions, emphasizing timely and accurate healthcare interventions.