A Computer-Assisted Diagnostic (CAD) of Screening Mammography to Detect Breast Cancer Without a Surgical Biopsy

2019 ◽  
Vol 11 (4) ◽  
pp. 31-49 ◽  
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Breast Cancer ◽  
Screening Mammography ◽  
Computer Assisted ◽  
Surgical Biopsy ◽  
Major Health Problem ◽  
Major Health ◽  
Cad System ◽  
Digital Database ◽  
The Social ◽  
N Gram

Breast cancer has become a major health problem in the world over the past 50 years and its incidence has increased in recent years. It accounts for 33% of all cancer cases, and 60% of new cases of breast cancer occur in women aged 50 to 74 years. In this work we have proposed a computer-assisted diagnostic (CAD) system that can predict whether a woman has cancer or not by analyzing her mammogram automatically without passing through a biopsy stage. The screening mammogram will be vectorized using the n-gram pixel representation. After the vectors obtained will be classified into one of the classes—with cancer or without cancer—using the social elephant algorithm. The experimentation using the digital database for screening mammography (DDSM) and validation measures—f-measure entropy recall, accuracy, specificity, RCT, ROC, AUC—show clearly the effectiveness and the superiority of our proposed bioinspired technique compared to others techniques existed in the literature such as naïve bayes, Knearest neighbours, and decision tree c4.5. The goal is to help radiologists with early detection to reduce the mortality rate among women with breast cancer.

scholarly journals Automated Assistance for Breast Cancer Identification on Mammograms Using Computer Vision Algorithms

2021 ◽  
Vol 12 (5) ◽  
pp. 1512-1520
Author(s):  
K. Nagaiah, Et. al.
Keyword(s):  
Breast Cancer ◽  
Low Cost ◽  
Screening Mammography ◽  
Support Vector ◽  
Cad Systems ◽  
Cad System ◽  
Digital Database ◽  
Detection Approach ◽  
Screening Strategies ◽  
Mammogram Image

One of the greatest health problems in the world is breast cancer. If these breast cancer abnormalities are identified early, there is a maximum chance of recovery. We can go for this early prediction. It is one of the most effective detection and screening strategies and is widely used. The basic goal of CAD systems is to support physicians in the process of diagnosis. CAD systems, however, are very expensive. Our emphasis is on developing a CAD system that is low-cost and effective. To categorize breast cancer as either benign or malignant, a computer-aided detection approach is suggested. The standard mammogram image corpus, Digital Database used for Screening Mammography, images are used for enhancement, segmented and GLCM, intensity and histogram methods are used to extract features. The work is carried out by effective multilayer perceptron classifier (MLP) and support vector machine (SVM). Compare the performance of the classifiers. The proposed approach achieved 96 % accuracy and 8% improvement in accuracy compared to previous approaches with same dataset [4].

Computer-Assisted Diagnosis System for Abnormalities Classification in Digital Mammography Based on Multi-Threshold Modified Local Ternary Pattern (MtMLTP)

2021 ◽  
Vol 49 ◽  
pp. 75-89
Author(s):  
Norhene Gargouri ◽  
Mouna Zouari ◽  
Randa Boukhris ◽  
Alima Damak ◽  
Dorra Sellami ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Tissue Characterization ◽  
Area Under The Curve ◽  
Breast Cancer Diagnosis ◽  
Screening Mammography ◽  
Computer Assisted ◽  
Local Ternary Pattern ◽  
Digital Database ◽  
Breast Tissues

The aim of this paper is to develop an efficient breast cancer Computer Aided Diagnosis (CAD) system allowing the analysis of different breast tissues in mammograms and performing textural classification (normal, mass or microcalcification). Although several feature extraction algorithms for breast tissues analysis have been used, the findings concerning tissue characterization show no consensus in the literature. Specifically, the challenge may be great for mass and microcalcification detection on dense breasts. The proposed system is based on the development of a new feature extraction approach, the latter is called Multi-threshold Modified Local Ternary Pattern (MtMLTP), it allows the discrimination between various tissues in mammographic images allowing significant improvements in breast cancer diagnosis. In this paper, we have used 1000 ROIs obtained from Digital Database for Screening Mammography (DDSM) database and 100 ROIs from a local Tunisian database named Tunisian Digital Database for Screening Mammography (TDDSM). The Artificial Neural Network (ANN) shows good performance in the classification of abnormalities since the Area Under the Curve (AUC) of the proposed system has been found to be 0.97 for the DDSM database and 0.99 for the TDDSM Database.

scholarly journals Danske og Internationale tendenser: Idræt og Sundhed

2015 ◽  
Vol 31 ◽  
pp. 99-110
Author(s):  
Thomas Skovgaard
Keyword(s):  
Physical Activity ◽  
Physical Inactivity ◽  
Built Environments ◽  
Environmental Determinants ◽  
Major Health Problem ◽  
Major Health ◽  
Strong Focus ◽  
Environmental Determinants Of Health ◽  
The Social ◽  
Health Enhancing Physical Activity

In the last decades there has been increasing recognition that physical inactivity represents a major health problem. Attention has been directed towards making the population more physical active in everyday life. Strategies have focused on individual, social and environmental determinants of health enhancing physical activity. This article argues that policies on physical activity, on top of addressing individual lifestyle factors, must include a strong focus on and plans for intervention in the social and built environments that influence the ability and interest in being physical active.

scholarly journals Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6201 ◽  
Author(s):  
Dina A. Ragab ◽  
Maha Sharkas ◽  
Stephen Marshall ◽  
Jinchang Ren
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Input Data ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

scholarly journals Potency of Combination of Fucoidan Microsphere and MiRNA-200b as Therapy in Chemoresistant Breast Cancer

2020 ◽  
Vol 2 (5) ◽  
Author(s):  
Sinta Wiranata ◽  
Made VW Yani ◽  
Agung Bagus S Satyarsa ◽  
I Ketut R Ardiana ◽  
Putu AT Adiputra
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Incidence ◽  
Migration And Invasion ◽  
Intrinsic Pathway ◽  
Extrinsic Pathway ◽  
Major Health Problem ◽  
Major Health ◽  
Mortality And Morbidity ◽  
Histone 3 ◽  
Apoptotic Signal

Breast cancer still become a major health problem in Indonesia and worldwide until today. Based on WHO 2012, breast cancer incidence is reported as 1.67 million cases with 90% of mortality rate in the metastasis stage. Chemoresistant is one cause of this increased mortality and morbidity. Nowadays, there are many treatment choices for cancer, but 90% incident of chemoresistant breast cancer occur even with prior chemotherapy. This review aimed to describe the potential of microsphere combinations fucoidan and miRNA-200b as a treatment for chemoresistant in breast cancer. Literature review were derived from scientific journals using www.pubmed.com and scholar.google.com database with “chemoresistant breast cancer, Fucoidan, microRNA-200b” as keyword. Fucoidan can induce apoptosis through the extrinsic pathway involving apoptotic receptor, or intrinsic pathway involving changes in mitochondrial membrane potential (MMP) to release cytochrome C and activating the apoptotic signal. Meanwhile, miRNA-200b expression, will decrease Sp1 expression and decrease histone-3 acetylation level in a miRNA-200b promoter, resulting in decreased cancer cell migration and invasion. However, no studies have evaluated this combination clinically. So, further studies are needed to confirm the potential of microsphere combination fucoidan and miRNA-200b in chemoresistant breast cancer.   Keywords: chemoresistant breast cancer; Fucoidan; miRNA-200b.

scholarly journals Breast Cancer Detection using Image Processing Techniques

2017 ◽  
Vol 10 (2) ◽  
pp. 391-399 ◽  
Author(s):  
Prannoy Giri ◽  
K. Saravanakumar
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Back Propagation ◽  
Texture Features ◽  
Screening Mammography ◽  
Breast Cancers ◽  
Back Propagation Algorithm ◽  
Digital Database ◽  
Processing Techniques ◽  
Aided Diagnosis

Breast Cancer is one of the significant reasons for death among ladies. Many research has been done on the diagnosis and detection of breast cancer using various image processing and classification techniques. Nonetheless, the disease remains as one of the deadliest disease. Having conceive one out of six women in her lifetime. Since the cause of breast cancer stays obscure, prevention becomes impossible. Thus, early detection of tumour in breast is the only way to cure breast cancer. Using CAD (Computer Aided Diagnosis) on mammographic image is the most efficient and easiest way to diagnosis for breast cancer. Accurate discovery can effectively reduce the mortality rate brought about by using mamma cancer. Masses and microcalcifications clusters are an important early symptoms of possible breast cancers. They can help predict breast cancer at it’s infant state. The image for this work is being used from the DDSM Database (Digital Database for Screening Mammography) which contains approximately 3000 cases and is being used worldwide for cancer research. This paper quantitatively depicts the analysis methods used for texture features for detection of cancer. These texture featuresare extracted from the ROI of the mammogram to characterize the microcalcifications into harmless, ordinary or threatening. These features are further decreased using Principle Component Analysis(PCA) for better identification of Masses. These features are further compared and passed through Back Propagation algorithm (Neural Network) for better understanding of the cancer pattern in the mammography image.

Cell-adhesion molecules as key mechanisms of tumor invasion: the case of breast cancer

2021 ◽  
Vol 21 ◽  
Author(s):  
Carla Luís ◽  
Raquel Soares ◽  
Rúben Fernandes ◽  
Mónica Botelho
Keyword(s):  
Breast Cancer ◽  
Cell Adhesion ◽  
Cardiovascular Diseases ◽  
Adhesion Molecules ◽  
Cell Adhesion Molecules ◽  
Tumor Invasion ◽  
Malignant Neoplasms ◽  
Major Health Problem ◽  
Major Health ◽  
Mortality And Morbidity

: Cancer is a major health problem worldwide and the second leading cause of death only overcome by cardiovascular diseases. Breast cancer is the leading cause of mortality and morbidity among women and one of the most common malignant neoplasms prompt to metastatic disease. In the present review, the mechanisms of the major cell adhesion molecules involved in tumor invasion are discussed, focusing in the case of breast cancer. A non-systematic updated revision of the literature was performed in order to assemble information regarding the expression of the adhesion cell molecules associated with metastasis.

scholarly journals Regulation of Exosomes in the Pathogenesis of Breast Cancer

2021 ◽  
Author(s):  
Congjian Shi ◽  
Hongqin Yang ◽  
Zhengchao Wang ◽  
Zhenghong Zhang
Keyword(s):  
Breast Cancer ◽  
Lipid Bilayers ◽  
Cell Types ◽  
Cancer Diagnostics ◽  
Major Health Problem ◽  
Major Health ◽  
Diagnostic Strategies ◽  
Therapeutic Tools ◽  
Common Malignancy

Extracellular vesicles (EVs) are a heterogeneous group of endogenous nanoscale vesicles that are secreted by various cell types. Based on their biogenesis and size distribution, EVs can be broadly classified as exosomes and microvesicles. Exosomes are enveloped by lipid bilayers with a size of 30–150 nm in diameter, which contain diverse biomolecules, including lipids, proteins and nucleic acids. Exosomes transport their bioactive cargoes from original cells to recipient cells, thus play crucial roles in mediating intercellular communication. Breast cancer is the most common malignancy among women and remains a major health problem worldwide, diagnostic strategies and therapies aimed at breast cancer are still limited. Growing evidence shows that exosomes are involved in the pathogenesis of breast cancer, including tumorigenesis, invasion and metastasis. Here, we provide a straightforward overview of exosomes and highlight the role of exosomes in the pathogenesis of breast cancer, moreover, we discuss the potential application of exosomes as biomarkers and therapeutic tools in breast cancer diagnostics and therapeutics.

scholarly journals Effectiveness and Efficacy of Vaccine on Mutated SARS-CoV-2 Virus and Post Vaccination Surveillance: A Narrative Review

Vaccines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 82
Author(s):  
Ihsanul Hafiz ◽  
Didi Nurhadi Illian ◽  
Okpri Meila ◽  
Ahmad Rusdan Handoyo Utomo ◽  
Arida Susilowati ◽  
...  
Keyword(s):  
Molecular Biology ◽  
Real World ◽  
Health Problem ◽  
Narrative Review ◽  
Health Science ◽  
Major Health Problem ◽  
Major Health ◽  
Post Vaccination ◽  
The Social ◽  
The World

The ongoing COVID-19 pandemic, as a result of the SARS-CoV-2 virus, since December 2019, is a major health problem and concern worldwide. The pandemic has impacted various fields, from the social to the development of health science and technology. The virus has been mutating and thus producing several new variants, rushing research in the field of molecular biology to develop rapidly to overcome the problems that occur. Vaccine clinical studies are developing promptly with the aim of obtaining vaccines that are effective in suppressing the spread of the virus; however, the development of viral mutations raises concerns about the decreasing effectiveness of the resulting vaccine, which also results in the need for more in-depth studies. There have been 330 vaccines developed, including 136 clinical developments and 194 pre-clinical developments. The SARS-CoV-2 variant continues to evolve today, and it poses a challenge in testing the effectiveness of existing vaccines. This is a narrative review describing the emergence of the COVID-19 pandemic, development of vaccine platforms, identification of concerning mutations and virus variants in various countries of the world, and real-world monitoring of post-vaccination effectiveness and surveillance.

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