| Title: | OS7-4 Hybrid Classical and Quantum Deep Learning Models for Medical Image Classification |
|---|---|
| Publication: | ICAROB2024 |
| Volume: | 29 |
| Pages: | 222-226 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2024.OS7-4 |
| Author(s): | Moona Mazher, Abdul Qayyum, M.K.A Ahamed Khan, Steven Niederer, Mastaneh Mokayef, Cik Suhana Hassan, Ridzuan A. |
| Publication Date: | February 22, 2024 |
| Keywords: | Quantum machine learning, Deep learning, Classification, Alzheimer's disease, Brain Tumour, Hybrid Quantum technique in medical imaging |
| Abstract: | Quantum machines enhance the capabilities of classical counterparts across various domains, notably in addressing real-world challenges. The classification of brain MR images for tumor detection is a crucial diagnostic process in the analysis of brain images. Traditional approaches, such as classical machine learning techniques and conventional deep learning structures like convolutional neural networks, are frequently employed for image classification. However, as the network size increases, training these models becomes increasingly arduous. Quantum algorithms offer advantages by optimizing the performance of classical algorithms through the incorporation of the intrinsic properties of quantum bits. In this paper, we proposed a hybrid classical and quantum convolutional neural network for Alzheimer's disease (AD) classification. The proposed model was further validated on the brain tumor classification task. The fundamental concept involves encoding data into quantum states, facilitating quicker information extraction, and subsequently utilizing this information to discern the data class. The proposed model results underscore the reliability and robustness and demonstrated by optimal performance accuracies across various datasets, the proposed model substantiates its efficacy in detecting and classifying AD disease and brain tumors. |
| PDF File: | https://alife-robotics.co.jp/members2024/icarob/data/html/data/OS/OS7-4.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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