scholarly journals Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
M. M. Mehdy ◽  
P. Y. Ng ◽  
E. F. Shair ◽  
N. I. Md Saleh ◽  
C. Gomes
Keyword(s):  
Breast Cancer ◽  
Medical Imaging ◽  
Cancer Detection ◽  
Large Time ◽  
Imaging Techniques ◽  
Breast Cancer Detection ◽  
Detection Techniques ◽  
Specificity And Sensitivity ◽  
Number Of Publications ◽  
Image Pattern

Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. Types of NN used, along with the various types of feeding data, have been reviewed. We also address hybrid NN adaptation in breast cancer detection.

scholarly journals Review of Microwaves Techniques for Breast Cancer Detection

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2390 ◽  
Author(s):  
Maged A. Aldhaeebi ◽  
Khawla Alzoubi ◽  
Thamer S. Almoneef ◽  
Saeed M. Bamatraf ◽  
Hussein Attia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Cost Effective ◽  
Breast Tumour ◽  
Microwave Imaging ◽  
Breast Cancer Detection ◽  
Alternative Methods ◽  
Breast Cancer Imaging ◽  
Detection Techniques ◽  
Microwave Techniques

Conventional breast cancer detection techniques including X-ray mammography, magnetic resonance imaging, and ultrasound scanning suffer from shortcomings such as excessive cost, harmful radiation, and inconveniences to the patients. These challenges motivated researchers to investigate alternative methods including the use of microwaves. This article focuses on reviewing the background of microwave techniques for breast tumour detection. In particular, this study reviews the recent advancements in active microwave imaging, namely microwave tomography and radar-based techniques. The main objective of this paper is to provide researchers and physicians with an overview of the principles, techniques, and fundamental challenges associated with microwave imaging for breast cancer detection. Furthermore, this study aims to shed light on the fact that until today, there are very few commercially available and cost-effective microwave-based systems for breast cancer imaging or detection. This conclusion is not intended to imply the inefficacy of microwaves for breast cancer detection, but rather to encourage a healthy debate on why a commercially available system has yet to be made available despite almost 30 years of intensive research.

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.

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.

Microwave Imaging Breast Cancer Detection Techniques: A Brief Review

2021 ◽  
pp. 203-210
Author(s):  
Monika Mathur ◽  
D. Mathur ◽  
G. Singh ◽  
S. K. Bhatnagar ◽  
Harshal Nigam ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Microwave Imaging ◽  
Breast Cancer Detection ◽  
Detection Techniques

scholarly journals Compact Wideband Microstrip Patch Antenna Design for Breast Cancer Detection

2021 ◽  
Vol 71 (03) ◽  
pp. 352-358
Author(s):  
Rakesh Singh ◽  
Naina Narang ◽  
Dharmendra Singh ◽  
Manoj Gupta
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Detection System ◽  
Breast Cancer Detection ◽  
Compact Antenna ◽  
Detection Techniques ◽  
Non Invasive ◽  
Impedance Contrast ◽  
Breast Phantom ◽  
Safety Limit

The current breast cancer detection techniques are mostly invasive and suffer from high cost, high false rate and inefficacy in early detection. These limitations can be subdued by development of non-invasive microwave detection system whose performance is predominantly dependent on the antenna used in the system. The designing of a compact wideband antenna and matching its impedance with breast phantom is a challenging task. In this paper, we have designed a compact antenna matched with the breast phantom operating in wideband frequency from 1 to 6 GHz capable to detect the dielectric (or impedance) contrast of the benign and malignant tissue. The impedance of the antenna is matched to a cubically shaped breast phantom and a very small tumor (volume=1 cm3). The antenna is tuned to the possible range of electrical properties of breast phantom and tumour (permittivity ranging from 10 to 20 and conductivity from 1.5 to 2.5 S/m). The return loss (S11), E-field distribution and specific absorption rate (SAR) are simulated. The operating band of antenna placed near the phantom without tumor was found to be (1.11-5.47)GHz and with tumor inside phantom is (1.29-5.50)GHz. Results also show that the SAR of the antenna is within the safety limit.

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.

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