Paper Title :A Feasible Study: Deep CNN for Middle Cerebral Artery Infarcts Detection in CT Perfusion Images using Hybrid Quantum Machine Learning

Author :Farha Naz F, Pharindra Sharma, Leena H.U

Article Citation :Farha Naz F ,Pharindra Sharma ,Leena H.U , (2022 ) " A Feasible Study: Deep CNN for Middle Cerebral Artery Infarcts Detection in CT Perfusion Images using Hybrid Quantum Machine Learning " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 7-11, Volume-10,Issue-4

Abstract : Abstract - Deep Convolutional Neural Networks is a strong image recognition technique in identifying the image dataset. Since, Deep CNN has shown excellent achievement in severalcutting-edge tasks, particularly those related to CT images and to address difficult problems, ordered layers are utilized to separate data patternsin the medical field of radiology. In this paper we explain the fundamentals of deep convolutional neural network models and quantum machine learning techniques. we identify the Middle Cerebral Artery infarcts in brain stroke CT scan images by applying CNN algorithms and quantum computing circuits. Then, we analyze the results of this research using quantum machine learning. Through these resultswe examine the benefits and drawbacks of integrating quantum machine learning technique in detecting brain stroke CT perfusion images of MCA infarcts in the future. Keywords - Deep Convolutional Neural Networks, Quantum Circuits, Quantum Machine learning, Middle Cerebral Artery

Type : Research paper

Published : Volume-10,Issue-4

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