Authors :
Uwizeyimana Leopoldine; Dr.Musoni Wilson
Volume/Issue :
Volume 8 - 2023, Issue 2 - February
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3SdMFnE
DOI :
https://doi.org/10.5281/zenodo.7652665
Abstract :
Experts predict that the use of artificial
intelligence and quantum computing will transform
medicine including medical imaging. One of the
common malignant tumors in women and seriously
threatens women’s physical and mental health is breast
cancer. The high incidence and mortality of breast
cancer are seriously threatening women’s physical and
mental health. The long time it took to get breast cancer’s
test result and the conditions which make delay without
being treated which caused the loss of lives due to latency.
Early screening for breast cancer via mammography,
ultrasound (US) and magnetic resonance imaging (MRI)
can significantly improve the prognosis of patients.
Artificial intelligence has been extensively researched for
breast cancer screening and has demonstrated good
performance in image identification tests. This research
was introduced analytical estimation for quantum
Convolutional Neural Network for breast cancer
detection, such as in the identification, segmentation and
classification of lesions, breast density assessment and
breast cancer risk assessment. Objectives will be to: To
detect the breast cancer on a quantum convolutional
neural network algorithm using image recognition, to
create and develop a CNN and QCNN algorithm that
will detect breast Cancer using Deep Learning and to
early predict pathological complete response to
neoadjuvant chemotherapy and survival analysis using
Deep Learning. Data was analyzed through the use of
descriptive and deep learning models like CNN and QCNN
models. This research used also Google Collab to analyze
the data and used data analysis tools python as
programming language. From the study results, we have
demonstrated the efficacy of quantum convolutional
neural networks (QCNNs) to detect breast cancer cells.
Using the techniques of deep learning and supervised
learning in the quantum framework, we have proposed
and tested the effectiveness of quantum CNN on the
quantum simulator available at the IBM quantum
experience platform.
Keywords :
Quantum Computing, Artificial Intelligence, Machine Learning, Deep Learning, Breast Cancer Detection, Medical Imaging.
Experts predict that the use of artificial
intelligence and quantum computing will transform
medicine including medical imaging. One of the
common malignant tumors in women and seriously
threatens women’s physical and mental health is breast
cancer. The high incidence and mortality of breast
cancer are seriously threatening women’s physical and
mental health. The long time it took to get breast cancer’s
test result and the conditions which make delay without
being treated which caused the loss of lives due to latency.
Early screening for breast cancer via mammography,
ultrasound (US) and magnetic resonance imaging (MRI)
can significantly improve the prognosis of patients.
Artificial intelligence has been extensively researched for
breast cancer screening and has demonstrated good
performance in image identification tests. This research
was introduced analytical estimation for quantum
Convolutional Neural Network for breast cancer
detection, such as in the identification, segmentation and
classification of lesions, breast density assessment and
breast cancer risk assessment. Objectives will be to: To
detect the breast cancer on a quantum convolutional
neural network algorithm using image recognition, to
create and develop a CNN and QCNN algorithm that
will detect breast Cancer using Deep Learning and to
early predict pathological complete response to
neoadjuvant chemotherapy and survival analysis using
Deep Learning. Data was analyzed through the use of
descriptive and deep learning models like CNN and QCNN
models. This research used also Google Collab to analyze
the data and used data analysis tools python as
programming language. From the study results, we have
demonstrated the efficacy of quantum convolutional
neural networks (QCNNs) to detect breast cancer cells.
Using the techniques of deep learning and supervised
learning in the quantum framework, we have proposed
and tested the effectiveness of quantum CNN on the
quantum simulator available at the IBM quantum
experience platform.
Keywords :
Quantum Computing, Artificial Intelligence, Machine Learning, Deep Learning, Breast Cancer Detection, Medical Imaging.
