AI Enhanced Medical Image and Pathology Analysis for Precision Diagnosis

By enabling quicker and more precise disease detection, artificial intelligence (AI) is transforming the medical imaging field and filling important gaps in diagnostic effectiveness and healthcare accessibility. In this paper, the thorough development, deployment, and assessment of a novel web-based artificial intelligence system that can use chest X-ray images to diagnose cardiomegaly, scoliosis, and pneumonia. Using a variety of carefully selected datasets from Roboflow for reliable model training, it combines cutting-edge deep convolutional neural networks (CNNs) with a contemporary, intuitive web interface. The frontend is constructed with React.js and styled with Tailwind CSS for an intuitive user experience, while the backend, driven by Flask, effectively handles inference requests through asynchronous RESTful APIs, ensuring seamless communication with the frontend, which is built using React.js and styled with Tailwind CSS for an intuitive user experience. This platform illustrates the profound feasibility of deploying scalable, intelligent diagnostic tools accessible via the web, highlighting their potential to augment traditional medical diagnostics and empower healthcare professionals with immediate, data-driven insights.

• Publication year

  2025

• Venue

  2025 First International Conference of Advances in Engineering and Computing Technologies for Sustainable Development (AECTSD)

• Publication date

  2025-12-11

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/AECTSD65988.2025.11410729
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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