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.

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 Density and tailored breast cancer screening: practice and prediction – an overview

2018 ◽  
Vol 7 (9) ◽  
pp. 205846011879121 ◽  
Author(s):  
Georg J Wengert ◽  
Thomas H Helbich ◽  
Panagiotis Kapetas ◽  
Pascal AT Baltzer ◽  
Katja Pinker
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
Breast Density ◽  
Cancer Detection ◽  
Public Awareness ◽  
Breast Cancer Detection ◽  
Screening Methods ◽  
Average Risk ◽  
Screening Practice

Mammography, as the primary screening modality, has facilitated a substantial decrease in breast cancer-related mortality in the general population. However, the sensitivity of mammography for breast cancer detection is decreased in women with higher breast densities, which is an independent risk factor for breast cancer. With increasing public awareness of the implications of a high breast density, there is an increasing demand for supplemental screening in these patients. Yet, improvements in breast cancer detection with supplemental screening methods come at the expense of increased false-positives, recall rates, patient anxiety, and costs. Therefore, breast cancer screening practice must change from a general one-size-fits-all approach to a more personalized, risk-based one that is tailored to the individual woman’s risk, personal beliefs, and preferences, while accounting for cost, potential harm, and benefits. This overview will provide an overview of the available breast density assessment modalities, the current breast density screening recommendations for women at average risk of breast cancer, and supplemental methods for breast cancer screening. In addition, we will provide a look at the possibilities for a risk-adapted breast cancer screening.

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 Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96946-96954 ◽  
Author(s):  
Jing Zheng ◽  
Denan Lin ◽  
Zhongjun Gao ◽  
Shuang Wang ◽  
Mingjie He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Adaboost Algorithm

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.

Nipple Fluid Carcinoembryonic Antigen and Prostate-Specific Antigen in Cancer-Bearing and Tumor-Free Breasts

2001 ◽  
Vol 19 (5) ◽  
pp. 1462-1467 ◽  
Author(s):  
Yashuang Zhao ◽  
Sigitas J. Verselis ◽  
Neil Klar ◽  
Norman L. Sadowsky ◽  
Carolyn M. Kaelin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Carcinoembryonic Antigen ◽  
Prostate Specific Antigen ◽  
Early Breast Cancer ◽  
Cancer Detection ◽  
Specific Antigen ◽  
Breast Cancer Detection ◽  
Screening Methods ◽  
Breast Lesions ◽  
Ductal Hyperplasia

PURPOSE: Mammograms and breast examinations are established methods for early breast cancer detection. Routine mammography screening reduces breast cancer mortality among women ages ≥ 50 years, but additional screening methods are needed. We and others have found high levels of carcinoembryonic antigen (CEA) and prostate-specific antigen (PSA) in nipple aspirate fluids (NAFs), but the usefulness for these bio-markers for early breast cancer detection is unknown. PATIENTS AND METHODS: NAFs from one or both breasts of 388 women were analyzed for CEA, PSA, and albumin levels. The study included 44 women with newly diagnosed invasive breast cancers, 67 women with proliferative breast lesions (ductal and lobular carcinoma in situ and atypical ductal hyperplasia), and 277 controls without these breast lesions. Analyses were conducted using the log10-transformed CEA and PSA levels to normalize the distributions of these tumor markers. RESULTS: Nipple fluid CEAs are significantly higher for cancerous breasts than tumor-free breasts (median 1,830 and 1,400 ng/mL, respectively; P < .01). However, at 90% specificity of the assay (CEA = 11,750 ng/mL), the corresponding sensitivity for cancer detection is 32%. CEA levels are not significantly different for breasts with proliferative lesions compared with tumor-free breasts. Nipple fluid PSAs do not differ by tumor status. Analyses of NAF albumin-standardized CEAs and PSAs yield similar results. Nipple fluid CEA and PSA titers are correlated in the affected and unaffected breast of women with unilateral lesions. CONCLUSION: Nipple fluid CEAs are higher for breasts with untreated invasive cancers, but the test sensitivity is low. Nipple fluid PSA titers do not seem to be useful for breast cancer detection.

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