Quantum-Enhanced AI System for Chest Disease Detection from Medical Reports and X-ray Images

Author

Mrs. B. Soujania, M. Hareesh Kumar, P. Deepika, T. Sai Ganesh, V. Kowshik Naidu

Abstract

 The growing need for intelligent healthcare systems has resulted in the use of AI systems for efficient and precise disease diagnosis. But most of the diagnostic systems available today consider only medical reports or only chest x-ray images. This can have an adverse impact on the efficiency of the diagnostic results and the clinical decision-making process. In order to overcome these problems, this project presents a Hybrid Quantum AI Framework for Disease Diagnosis Using Medical Reports and Chest X-ray Images, combining Machine Learning (ML), Deep Learning (DL), and Quantum Machine Learning (QML) into one healthcare system. In the proposed system, users can upload their medical reports and chest x-ray images using a web-based application. For processing medical reports, OCR and NLP technologies will be used, whereas chest x-ray images will be processed using CNNs. The disease classification is done by applying quantum machine learning models such as Quantum Support Vector Machines or Variational Quantum Classifiers through Qiskit and PennyLane on the fused features. It also assesses the performance of both quantum and classical machine learning models and makes a comparison between them to identify their efficiency. Lastly, it outputs the predicted disease name, confidence score, and a diagnosis summary that can be easily understood by healthcare professionals. This framework is capable of utilizing the power of hybrid quantum-classical intelligence for computer-aided diagnosis systems.

Keywords

Variational Quantum Classifier, Quantum Neural Network, Optical Character Recognition (OCR), Natural Language Processing (NLP), Convolutional Neural Networks (CNN).

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References

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