A QUANTUM REMOTE SENSING IN EARTH OBSERVATIONS – FROM CLASSICAL TO QUANTUM DEEP LEARNING

Fan Fan, Xiao Xiang Zhu, Technische Universität München

Muhammad Hussain Habib Raazia Sosan Waseem

DR SELVANI DEEPTHI KAVILA, P. HEMA VENKATA RAMANA, SELVA MALAR N, CHILAKALAPUDI MALATHI, DR DOKKU DURGA BHAVANI, DORABABU SUDARSA, N. SRIKANTH, MYLAVARAPU KALYAN RAM

Early and accurate recognition of cardiovascular disease (CVD) using medical imaging is essential for timely clinical intervention and management. The objective of this study was to continue developing a hybrid quantum-classical deep learning system for the early detection and automatic triage of cardiovascular diseases using cardiac imaging information. The proposed method consists of a combination of efficient feature extraction via a lightweight classical convolutional neural network and a quantum-illness feature transformation and decision-refinement process implemented as a variational quantum circuit. A clinically informed triage module was added to rate patients into three risk categories (low-, moderate-, and high-risk). Experimental evaluation on a public cardiac magnetic resonance imaging (MRI) dataset revealed that the proposed framework achieved 94.5% classification accuracy and an area under the curve (AUC) of 0.96, with sensitivity exceeding that of state-of-the-art classical deep learning models. The results show that quantum-enhanced feature representations provide better class separability, robustness to Imaging noise, and improved learning efficiency. The results suggest that one way to overcome the limitations of conventional deep models is the hybrid quantum-classical learning approach, which is also compatible with near-term quantum hardware. This work raises the question of whether quantum-assisted artificial intelligence can enable scalable, real-time cardiovascular decision support systems and promote next-generation intelligent healthcare systems.

Mihai P. Datcu

Nour Aburaed, Faisal Shah Khan, Mohammed Q. Alkhatib

Upasna Tiwari, Sahil Panwar, Rijul Sharma

Medical image diagnosis has become an integral part of the medical field, which helps the doctor diagnose the disease using various medical image techniques like X-ray, computed tomography, and magnetic resonance imaging. The recent advancements in artificial intelligence and deep learning techniques have improved the efficiency of the medical image diagnosis using artificial intelligence and machine learning techniques. However, the large-scale requirement of the data has raised many issues related to the privacy and security of the data in the medical field. In this regard, the concept of privacy-preserving artificial intelligence has been introduced to ensure the security of the data in the artificial intelligence system. Classical machine learning techniques are limited in handling the complexity of the data and the efficiency of the system. In this regard, the concept of quantum computing has been introduced, which helps the system process the data using the superposition property of the system. The proposed approach also integrates principles of data science and cyber security to ensure efficient data handling and robust protection against emerging digital threats