Breast Cancer Detection Techniques: Issues and Challenges

2019 ◽  
Vol 100 (4) ◽  
pp. 379-386 ◽  
Author(s):  
Poonam Jaglan ◽  
Rajeshwar Dass ◽  
Manoj Duhan
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Detection Techniques

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.

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 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 Breast Cancer Detection using Deep Learning Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 3959-3963
Author(s):  
Prof. M. S. Choudhari
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Survival Rate ◽  
Cancer Detection ◽  
Deep Neural Network ◽  
The United States ◽  
Breast Cancer Detection ◽  
Detection Techniques ◽  
Learning Techniques

Breast cancer is the most common form of cancer among women and the second most common cancer in the world (an estimated 1 152 161 new cases per year), trailing only lung cancer .The current approach to this disease involves early detection and treatment. This approach in the United States yields an 85% 10-year survival rate. Survival is directly related to stage at diagnosis, as can be seen by a 98% 10- year survival rate for patients with stages 0 and I disease compared with a 65% 10-year survival rate for patients with stage III disease. To improve survival in this disease, more patients need to be identified at an early stage.Therefore, we evaluated existing and emerging technologies used for breast cancer screening and detection to identify areas for potential improvement. The main criteria for a good screening test are accuracy, high sensitivity, ease of use, acceptability to the population being screened (with regard to discomfort and time), and low cost. We can begins by describing commonly used breast cancer detection techniques and then delves into emerging modalities. Several studies addressing breast cancer using Deep learning techniques. Many claim that their algorithms are faster, easier, or more accurate than others . This system is based on thermal image processing and Deep learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this was to optimize the learning algorithm. In this system , we applied the deep neural network technique to select the best features and perfect parameter values of the deep machine learning. The present study proves that deep neural network can automatically find the best model by combining feature preprocessing methods and classification algorithms.

scholarly journals Breast Ultrasound Elastography is the Most Mature and Well-documented Method for the Measurement of Tissue Stiffness: A Review

2021 ◽  
Vol 16 (1) ◽  
pp. 42-44
Author(s):  
Hafizur Rahman
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Cancer Diagnosis ◽  
Imaging Technique ◽  
Breast Cancer Diagnosis ◽  
Breast Cancer Detection ◽  
Ultrasound Elastography ◽  
Tissue Stiffness ◽  
Detection Techniques ◽  
Non Invasive

Breast cancer is the most common malignancy and one of the leading causes of death in females worldwide. North America has one of the highest incidence breast cancer rates in the world, making breast cancer awareness a high priority. Only in the USA, 527 women are expected to be diagnosed with breast cancer while 110 women will die of it per day. Central to the importance of breast cancer diagnosis is the fact that almost one-third of the latter group could survive if their cancer is detected and treated early. In a worldwide context, this translates into nearly 400,000 lives that could be saved every year as a result of early detection. As such; developing technique that can help to detect and diagnose breast cancer at early stage can have a great impact on survival and quality of life of breast cancer patients. Conventional breast cancer screening and detection techniques such as clinical breast examination and X- ray mammography are known to have low sensitivity. Breast magnetic resonance imaging (MRI) is more sensitive modality for breast cancer detection, however, MRI is costly and has been shown to have low specificity for breast cancer diagnosis. Dynamic contrast-enhanced MRI has been demonstrated to provide a good sensitivity and specificity for differentiation of benign versus malignant lesions, due to altered angiogenesis mechanisms in tumors. However, in addition to being costly, requires injection of exogenous contrast agents to provide such contrast. An alternate imaging technique for breast cancer detection employs tissue stiffness as contrast mechanism. The technique is founded on the fact that alterations in breast tissue stiffness are frequently associated with pathology. Ultrasound elastography is the most mature and well-documented method for the measurement of tissue stiffness. Elastographybased imaging technique has received substantial attention in recent years for non-invasive assessment of tissue mechanical properties. These techniques take advantage of changed soft tissue elasticity in various pathologies to yield qualitative and quantitative information that can be used for diagnostic purpose. Measurements are acquired in specialized imaging modes that can detect tissue stiffness in response to an applied mechanical force. Ultrasoundbased methods are of particular interest due to its many inherent advantages, such as wide availability including at the bedside and relatively low cost. While ultrasound elastography has shown promising results for non-invasive assessment of breast stiffness is emerging. Faridpur Med. Coll. J. 2021;16(1):42-44

scholarly journals Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review

2021 ◽  
Vol 11 (22) ◽  
pp. 10753
Author(s):  
Ahmad Ashraf Abdul Halim ◽  
Allan Melvin Andrew ◽  
Mohd Najib Mohd Yasin ◽  
Mohd Amiruddin Abd Rahman ◽  
Muzammil Jusoh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Ultra Sound ◽  
Screening Methods ◽  
Detection Techniques ◽  
Clinical Detection ◽  
Magnetic Resonance Imaging Mri ◽  
Detection Technologies

Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.

Sign in / Sign up

Export Citation Format

Share Document