Abstract
An early diagnosis of any cancer is essential for better human survival. Esophageal cancer is the most common cancer with more than 80%, 5-year relative survival rate. This cancer has become so common due to the changes in the eating habit of the younger population. Recent advancements in medical screening techniques have improved the detection of this cancer. However, the real challenge lies in identifying the infected area, as it depends on the expert hypothesis and diagnosis. This research paper used a hybrid Quantum Convolution neural network (QCNN) model to identify infected areas effectively. QCNN model can process a large amount of multi-dimension data in parallel. To achieve the desired efficiency instead of traditional filters, quantum filters are used in the convolution layer, so in-depth feature analysis can be carried out to determine the early stages of cancer. For the experiment, around 5500 healthy and cancer images were used. Compared with the classic CNN model, the proposed hybrid QCNN provides better accuracy in the early prediction of cancer image.
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Minu, R.I., Margala, M., Shankar, S.S. et al. Early-stage esophageal cancer detection using hybrid quantum CNN. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08333-3
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DOI: https://doi.org/10.1007/s00500-023-08333-3