scholarly journals Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Sushovan Chaudhury ◽  
Manik Rakhra ◽  
Naz Memon ◽  
Kartik Sau ◽  
Melkamu Teshome Ayana
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Detection System ◽  
Imaging Techniques ◽  
Breast Cancer Mortality ◽  
Breast Cancer Diagnosis ◽  
Breast Cancer Detection ◽  
Surrounding Tissue ◽  
Suggested Approach ◽  
Cancer Agent

Breast cancer is a strong risk factor of cancer amongst women. One in eight women suffers from breast cancer. It is a life-threatening illness and is utterly dreadful. The root cause which is the breast cancer agent is still under research. There are, however, certain potentially dangerous factors like age, genetics, obesity, birth control, cigarettes, and tablets. Breast cancer is often a malignant tumor that begins in the breast cells and eventually spreads to the surrounding tissue. If detected early, the illness may be reversible. The probability of preservation diminishes as the number of measurements increases. Numerous imaging techniques are used to identify breast cancer. This research examines different breast cancer detection strategies via the use of imaging techniques, data mining techniques, and various characteristics, as well as a brief comparative analysis of the existing breast cancer detection system. Breast cancer mortality will be significantly reduced if it is identified and treated early. There are technological difficulties linked to scans and people’s inconsistency with breast cancer. In this study, we introduced a form of breast cancer diagnosis. There are different methods involved to collect and analyze details. In the preprocessing stage, the input data picture is filtered by using a window or by cropping. Segmentation can be performed using k -means algorithm. This study is aimed at identifying the calcifications found in bosom cancer in the last phase. The suggested approach is already implemented in MATLAB, and it produces reliable performance.

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.

Multimodal Ultrasonic Imaging for Breast Cancer Detection

2012 ◽  
Vol 37 (3) ◽  
pp. 253-260 ◽  
Author(s):  
Jorge Camacho ◽  
Luis Medina ◽  
Jorge F. Cruza ◽  
José M. Moreno ◽  
Carlos Fritsch
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Current Practice ◽  
Ultrasonic Transducer ◽  
Screening Method ◽  
Phase Coherence ◽  
Breast Cancer Detection ◽  
Surrounding Tissue ◽  
Dense Breast Tissue ◽  
Breast Phantom

Abstract Ultrasound is used for breast cancer detection as a technique complementary to mammography, the standard screening method. Current practice is based on reflectivity images obtained with conventional instruments by an operator who positions the ultrasonic transducer by hand over the patient’s body. It is a non-ionizing radiation, pain-free and not expensive technique that provides a higher contrast than mammography to discriminate among fluid-filled cysts and solid masses, especially for dense breast tissue. However, results are quite dependent on the operator’s skills, images are difficult to reproduce, and state-of-the-art instruments have a limited resolution and contrast to show micro-calcifications and to discriminate between lesions and the surrounding tissue. In spite of their advantages, these factors have precluded the use of ultrasound for screening. This work approaches the ultrasound-based early detection of breast cancer with a different concept. A ring array with many elements to cover 360◦ around a hanging breast allows obtaining repeatable and operator-independent coronal slice images. Such an arrangement is well suited for multi-modal imaging that includes reflectivity, compounded, tomography, and phase coherence images for increased specificity in breast cancer detection. Preliminary work carried out with a mechanical emulation of the ring array and a standard breast phantom shows a high resolution and contrast, with an artifact-free capability provided by phase coherence processing.

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 Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2799 ◽  
Author(s):  
Sebastien Mambou ◽  
Petra Maresova ◽  
Ondrej Krejcar ◽  
Ali Selamat ◽  
Kamil Kuca
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Infrared Image ◽  
Breast Cancer Diagnosis ◽  
Diagnostic Techniques ◽  
Breast Cancer Detection ◽  
Thermal Activity ◽  
Infrared Thermal Imaging ◽  
Detection Techniques

Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models.

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

Comments on the Logistic Function in ROC Analysis: Applications to Breast Cancer Detection

1982 ◽  
Vol 21 (01) ◽  
pp. 26-30 ◽  
Author(s):  
Joann Haberman ◽  
J. E. Goin
Keyword(s):  
Breast Cancer ◽  
Diagnostic Imaging ◽  
Cancer Detection ◽  
Task Analysis ◽  
Roc Analysis ◽  
Imaging Techniques ◽  
Logistic Function ◽  
Detection Task ◽  
Breast Cancer Detection ◽  
Statistical Methodology

ROC analysis is rapidly becoming the method of choice for determining the performance of diagnostic imaging techniques. This discussion is intended to provide scientists working in diagnostic imaging with a more in-depth view of the detection task analysis than is currently available in the statistical methodology literature. The logistic function is defined and its utility is illustrated by analyzing two breast cancer detection modalities.

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