A 7.21-bit ENOB 100MS/s 1.51mW Inverter based Pipelined ADC with Digital Calibration for Breast Cancer Detection System

2020 ◽  
Vol 140 (6) ◽  
pp. 585-591
Author(s):  
Yoshihiro Masui ◽  
Tsukasa Nishimiya ◽  
Allex Uemi ◽  
Akihiro Toya ◽  
Takamaro Kikkawa
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Detection System ◽  
Breast Cancer Detection ◽  
Digital Calibration ◽  
Pipelined Adc

scholarly journals Deep learning model for fully automated breast cancer detection system from thermograms

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262349
Author(s):  
Esraa A. Mohamed ◽  
Essam A. Rashed ◽  
Tarek Gaber ◽  
Omar Karam
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Detection System ◽  
Diagnostic Technique ◽  
Learning Model ◽  
The Body ◽  
Breast Cancer Detection ◽  
Benchmark Database ◽  
Deep Learning Model

Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

scholarly journals Compact Reflectometers for a Wideband Microwave Breast Cancer Detection System

2006 ◽  
Vol 2 (3) ◽  
Author(s):  
Marek Bialkowski ◽  
Norhudah Seman ◽  
Amin Abbosh ◽  
Wee Chang Khor
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Detection System ◽  
Synthesis Method ◽  
Breast Cancer Detection ◽  
Power Divider ◽  
Frequency Synthesis ◽  
Step Frequency ◽  
Power Dividers ◽  
Time Space

The design of compact wideband microwave reflectometers for the purpose of inclusion in a breast cancer detection system is presented. In this system, a wideband frequency source is used to synthesize a narrow pulse via the step-frequency synthesis method. The reflectometer undertakes measurements in the frequency domain and the collected data is transformed into the time/space domain using IFFT. In order to accomplish reflection coefficient measurements over a large frequency band, compact wideband couplers and power dividers are used to form the reflectometer. Two compact six-port reflectometer configurations are investigated. One uses the Lange coupler and the Gysel power divider and the other one employs a 3dB slot-coupled microstrip coupler and a 2-stage Wilkinson power divider. The reflectometer employing the slot-coupled coupler and the Wilkinson divider provides a wider operational bandwidth, as shown by simulation results performed with Agilent ADS.

Computerized Breast Cancer Detection System

2014 ◽  
Vol 11 (2) ◽  
pp. 907-910
Author(s):  
R.J. Hemalatha ◽  
G. Hari Krishnan ◽  
G. Umashankar ◽  
Sheeba Abraham
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Detection System ◽  
Breast Cancer Detection

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 A portable breast cancer detection system based on smartphone with infrared camera

2019 ◽  
Vol 26 ◽  
pp. 57-63 ◽  
Author(s):  
Jian Ma ◽  
Pengchao Shang ◽  
Chen Lu ◽  
Safa Meraghni ◽  
Khaled Benaggoune ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Detection System ◽  
Infrared Camera ◽  
Breast Cancer Detection

scholarly journals Design and evaluation of a novel breast cancer detection system combining both thermoacoustic (TA) and photoacoustic (PA) tomography

2008 ◽  
Vol 35 (6Part1) ◽  
pp. 2218-2223 ◽  
Author(s):  
Manojit Pramanik ◽  
Geng Ku ◽  
Changhui Li ◽  
Lihong V. Wang
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Detection System ◽  
Breast Cancer Detection
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