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This work is licensed under a Creative Commons Attribution 4.0 International License.
MediScan AI: A Python based Intelligent System for Chest Disease Detection and Diagnosis.
Mr. H.M. Gaikwad, Sahil Rajbanshi, Priya Vaishnav, Om Shinde, Nirjala Kachave
DOI: 10.17148/IJARCCE.2026.15326
Abstract: MediScan AI is an advanced AI-powered chest disease detection and clinical decision support system designed to enhance diagnostic accuracy and optimize healthcare workflows. The system integrates the EfficientNetV2B0 deep learning architecture with Explainable AI (Grad-CAM) and Google Gemini AI to provide accurate multi-class chest disease classification and automated clinical recommendations.
The proposed system detects over 50 chest pathologies—including Pneumonia, Tuberculosis, Lung Cancer, and COVID-19—from chest X-ray images with a classification accuracy of 99.2%. Grad-CAM heatmap visualization improves model transparency by highlighting disease-affected regions, enabling radiologists to validate AI decisions.
The architecture consists of a Flask-based backend for model deployment and a React 19 frontend for real-time interaction and patient management. Additionally, Google Gemini AI enhances the system by generating structured treatment suggestions and preliminary prescriptions, transforming the platform into a Clinical Decision Support System (CDSS).
MediScan AI bridges the gap between medical imaging and intelligent automation, reducing diagnostic errors, improving workflow efficiency, and supporting healthcare professionals with explainable and reliable AI assistance.
Keywords: Chest Disease Detection, EfficientNetV2, Deep Learning, Medical Imaging, Grad-CAM, Explainable AI (XAI), Clinical Decision Support System (CDSS), Flask, ReactJS, Google Gemini AI
The proposed system detects over 50 chest pathologies—including Pneumonia, Tuberculosis, Lung Cancer, and COVID-19—from chest X-ray images with a classification accuracy of 99.2%. Grad-CAM heatmap visualization improves model transparency by highlighting disease-affected regions, enabling radiologists to validate AI decisions.
The architecture consists of a Flask-based backend for model deployment and a React 19 frontend for real-time interaction and patient management. Additionally, Google Gemini AI enhances the system by generating structured treatment suggestions and preliminary prescriptions, transforming the platform into a Clinical Decision Support System (CDSS).
MediScan AI bridges the gap between medical imaging and intelligent automation, reducing diagnostic errors, improving workflow efficiency, and supporting healthcare professionals with explainable and reliable AI assistance.
Keywords: Chest Disease Detection, EfficientNetV2, Deep Learning, Medical Imaging, Grad-CAM, Explainable AI (XAI), Clinical Decision Support System (CDSS), Flask, ReactJS, Google Gemini AI
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This work is licensed under a Creative Commons Attribution 4.0 International License.How to Cite:
[1] Mr. H.M. Gaikwad, Sahil Rajbanshi, Priya Vaishnav, Om Shinde, Nirjala Kachave, “MediScan AI: A Python based Intelligent System for Chest Disease Detection and Diagnosis.,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.15326