scholarly journals Breast Cancer Detection Using Quantum Convolutional Neural Networks: A Demonstration on a Quantum Computer

2020 ◽  
Author(s):  
Nilima Mishra ◽  
Aradh Bisarya ◽  
Shubham Kumar ◽  
Walid El Maouaki ◽  
Sabyasachi Mukhopadhyay ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Supervised Learning ◽  
Cancer Detection ◽  
Quantum Computer ◽  
Classical Counterpart ◽  
Cell Data ◽  
Quantum Framework

Deep learning have paved the way for scientists to achieve great technical feats. In an endeavor to hone and perfect these techniques, quantum deep learning is a promising and important tool to utilize to the fullest. Using the techniques of deep learning and supervised learning in a quantum framework, we are able to propose a quantum convolutional neural network and showcase its implementation. Using the techniques of deep learning and supervised learning in a quantum framework, we are able to propose a quantum convolutional neural network and showcase its implementation. We keep our focus on training of the ten qubits system in a way so that it can learn from labeling of the breast cell data of Wisconsin breast cancer database and optimize the circuit parameters to obtain the minimum error. Through our study, we also have showcased that quantum convolutional neural network can outperform its classical counterpart not only in terms of accuracy but also in the aspect of better time complexity.

scholarly journals Breast Cancer Detection using Residual Convolutional Neural Network and Weighted Loss

2019 ◽  
Vol 11 (2) ◽  
pp. 43
Author(s):  
Samuel Aji Sena ◽  
Panca Mudjirahardjo ◽  
Sholeh Hadi Pramono
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Ductal Carcinoma ◽  
Detection System ◽  
Breast Cancer Detection ◽  
University Of Pennsylvania ◽  
Training Approach

This research presents a breast cancer detection system using deep learning method. Breast cancer detection in a large slide of biopsy image is a hard task because it needs manual observation by a pathologist to find the malignant region. The deep learning model used in this research is made up of multiple layers of the residual convolutional neural network, and instead of using another type of classifier, a multilayer neural network was used as the classifier and stacked together and trained using end-to-end training approach. The system is trained using invasive ductal carcinoma dataset from the Hospital of the University of Pennsylvania and The Cancer Institute of New Jersey. From this dataset, 80% and 20% were randomly sampled and used as training and testing data respectively. Training a neural network on an imbalanced dataset is quite challenging. Weighted loss function was used as the objective function to tackle this problem. We achieve 78.26% and 78.03% for Recall and F1-Score metrics, respectively which are an improvement compared to the previous approach.

scholarly journals Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129889-129898
Author(s):  
Xin Dong ◽  
Yizhao Zhou ◽  
Lantian Wang ◽  
Jingfeng Peng ◽  
Yanbo Lou ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Liver Cancer ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Learning Framework

Pre-Trained Convolutional Neural Networks for Breast Cancer Detection Using Ultrasound Images

2021 ◽  
Vol 21 (4) ◽  
pp. 1-17
Author(s):  
Mehedi Masud ◽  
M. Shamim Hossain ◽  
Hesham Alhumyani ◽  
Sultan S. Alshamrani ◽  
Omar Cheikhrouhou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Early Detection Of Cancer ◽  
Fine Tuning ◽  
Volunteer Computing ◽  
Ultrasound Images ◽  
Key Features

Volunteer computing based data processing is a new trend in healthcare applications. Researchers are now leveraging volunteer computing power to train deep learning networks consisting of billions of parameters. Breast cancer is the second most common cause of death in women among cancers. The early detection of cancer may diminish the death risk of patients. Since the diagnosis of breast cancer manually takes lengthy time and there is a scarcity of detection systems, development of an automatic diagnosis system is needed for early detection of cancer. Machine learning models are now widely used for cancer detection and prediction research for improving the successive therapy of patients. Considering this need, this study implements pre-trained convolutional neural network based models for detecting breast cancer using ultrasound images. In particular, we tuned the pre-trained models for extracting key features from ultrasound images and included a classifier on the top layer. We measured accuracy of seven popular state-of-the-art pre-trained models using different optimizers and hyper-parameters through fivefold cross validation. Moreover, we consider Grad-CAM and occlusion mapping techniques to examine how well the models extract key features from the ultrasound images to detect cancers. We observe that after fine tuning, DenseNet201 and ResNet50 show 100% accuracy with Adam and RMSprop optimizers. VGG16 shows 100% accuracy using the Stochastic Gradient Descent optimizer. We also develop a custom convolutional neural network model with a smaller number of layers compared to large layers in the pre-trained models. The model also shows 100% accuracy using the Adam optimizer in classifying healthy and breast cancer patients. It is our belief that the model will assist healthcare experts with improved and faster patient screening and pave a way to further breast cancer research.

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