Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study

Risk assessment and prevention of breast cancer

One woman in eight develops breast cancer during her lifetime in the Western world. Measures are warranted to reduce mortality and to prevent breast cancer. Mammography screening reduces mortality by early detection. However, approximately one fourth of the women who develop breast cancer are diagnosed within two years after a negative screen. There is a need to identify the short-term risk of these women to better guide clinical followup. Another drawback of mammography screening is that it focuses on early detection only and not on breast cancer prevention. Today, it is known that women attending screening can be stratified into high and low risk of breast cancer. Women at high risk could be offered preventive measures such as low-dose tamoxifen to reduce breast cancer incidence. Women at low risk do not benefit from screening and could be offered less frequent screening. In study I, we developed and validated the mammographic density measurement tool STRATUS to enable mammogram resources at hospitals for large scale epidemiological studies on risk, masking, and therapy response in relation to breast cancer. STRATUS showed similar measurement results on different types of mammograms at different hospitals. Longitudinal studies on mammographic density could also be analysed more accurate with less nonbiological variability. In study II, we developed and validated a short-term risk model based on mammographic features (mammographic density, microcalcifications, masses) and differences in occurrences of mammographic features between left and right breasts. The model could optionally be expanded with lifestyle factors, family history of breast cancer, and genetic determinants. Based on the results, we showed that among women with a negative mammography screen, the short-term risk tool was suitable to identify women that developed breast cancer before or at next screening. We also showed that traditional long-term risk models were less suitable to identify the women who in a short time-period after risk assessment were diagnosed with breast cancer. In study III, we performed a phase II trial to identify the lowest dose of tamoxifen that could reduce mammographic density, an early marker for reduced breast cancer risk, to the same extent as standard 20 mg dose but cause less side-effects. We identified 2.5 mg tamoxifen to be non-inferior for reducing mammographic density. The women who used 2.5 mg tamoxifen also reported approximately 50% less severe vasomotor side-effects. In study IV, we investigated the use of low-dose tamoxifen for an additional clinical use case to increase screening sensitivity through its effect on reducing mammographic density. It was shown that 24% of the interval cancers have a potential to be detected at prior screen. In conclusion, tools were developed for assessing mammographic density and breast cancer risk. In addition, two low-dose tamoxifen concepts were developed for breast cancer prevention and improved screening sensitivity. Clinical prospective validation is further needed for the risk assessment tool and the low-dose tamoxifen concepts for the use in breast cancer prevention and for reducing breast cancer mortality

Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170.

We analyzed 3,872 common genetic variants across the ESR1 locus (encoding estrogen receptor α) in 118,816 subjects from three international consortia. We found evidence for at least five independent causal variants, each associated with different phenotype sets, including estrogen receptor (ER(+) or ER(-)) and human ERBB2 (HER2(+) or HER2(-)) tumor subtypes, mammographic density and tumor grade. The best candidate causal variants for ER(-) tumors lie in four separate enhancer elements, and their risk alleles reduce expression of ESR1, RMND1 and CCDC170, whereas the risk alleles of the strongest candidates for the remaining independent causal variant disrupt a silencer element and putatively increase ESR1 and RMND1 expression.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/ng.352

A review on automatic mammographic density and parenchymal segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models

Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated

A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules

Our goal in this paper is to automatically extract a set of decision rules (rule set) that best explains a classification data set. First, a large set of decision rules is extracted from a set of decision trees trained on the data set. The rule set should be concise, accurate, have a maximum coverage and minimum number of inconsistencies. This problem can be formalized as a modified version of the weighted budgeted maximum coverage problem, known to be NP-hard. To solve the combinatorial optimization problem efficiently, we introduce a nested genetic algorithm which we then use to derive explanations for ten public data sets

Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository

Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most efficient and widely used supervised learning algorithms are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor (LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this paper is to implement these elegant learning algorithms on eleven different datasets from the UCI machine learning repository to observe the variation of accuracies for each of the algorithms on all datasets. Analyzing the accuracy of the algorithms will give us a brief idea about the relationship of the machine learning algorithms and the data dimensionality. All the algorithms are developed in Matlab. Upon such accuracy observation, the comparison can be built among KNN, SVM, LMNN, and ENN regarding their performances on each dataset.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018