scholarly journals A hybrid feature selection on AIRS method for identifying breast cancer diseases

2021 ◽  
Vol 11 (1) ◽  
pp. 728
Author(s):  
Achmad Ridok ◽  
Nashi Widodo ◽  
Wayan Firdaus Mahmudy ◽  
Muhaimin Rifa’i
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Breast Tissue ◽  
Information Gain ◽  
Classification Problem ◽  
Accurate Identification ◽  
Fine Needle Aspirates ◽  
Cell Pattern ◽  
Invasive Method ◽  
Cancer Diseases

Breast cancer may cause a death due to the late diagnosis. A cheap and accurate tool for early detection of this disease is essential to prevent fatal incidence. In general, the cheap and less invasive method to diagnose the disease could be done by biopsy using fine needle aspirates from breast tissue. However, rapid and accurate identification of the cancer cell pattern from the cell biopsy is still challenging task. This diagnostic tool can be developed using machine learning as a classification problem. The performance of the classifier depends on the interrelationship between sample sizes, some features, and classifier complexity. Thus, the removal of some irrelevant features may increase classification accuracy. In this study, a new hybrid feature selection fast correlation based feature (FCBF) and information gain (IG) was used to select features on identifying breast cancer using AIRS algorithm. The results of 10 times the crossing (CF) of our validation on various AIRS seeds indicate that the proposed method can achieve the best performance with accuracy =0.9797 and AUC=0.9777 at k=6 and seed=50.

AUTOMATIC SEGMENTATION OF BREAST TISSUE THERMAL IMAGES

2018 ◽  
Vol 30 (03) ◽  
pp. 1850024 ◽  
Author(s):  
Zeinab Heidari ◽  
Mehrdad Dadgostar ◽  
Zahra Einalou
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Imaging ◽  
Breast Tissue ◽  
Early Stage ◽  
Automatic Segmentation ◽  
Image Data ◽  
Thermal Images ◽  
Early Stages ◽  
Invasive Method

Breast cancer is one of the main causes of women’s death. Thermal breast imaging is one the non-invasive method for cancer at early stage diagnosis. In contrast to mammography this method is cheap and painless and it can be used during pregnancy while ionized beams are not used. Specialists are seeking new ways to diagnose the cancer in early stages. Segmentation of the breast tissue is one of the most indispensable stages in most of the cancer diagnosis methods. By the advancement of infrared precise cameras, new and fast computers and nouvelle image processing approaches, it is feasible to use thermal imaging for diagnosis of breast cancer at early stages. Since the breast form is different in individuals, image segmentation is a hard task and semi-automatic or manual methods are usual in investigations. In this research the image data base of DMR-IR has been utilized and a now automatic approach has been proposed which does not need learning. Data were included 159 gray images used by dynamic protocol (132 healthy and 27 patients). In this study, by combination of different image processing methods, the segmentation of thermal images of the breast tissues have been completed automatically and results show the proper performance of recommended method.

scholarly journals On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction

2021 ◽  
Vol 11 (14) ◽  
pp. 6574
Author(s):  
Min-Wei Huang ◽  
Chien-Hung Chiu ◽  
Chih-Fong Tsai ◽  
Wei-Chao Lin
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Prediction Model ◽  
Performance Improvement ◽  
Prediction Models ◽  
Information Gain ◽  
Class Imbalance ◽  
Sampling Technique ◽  
Sampling Techniques ◽  
Cancer Prediction

Breast cancer prediction datasets are usually class imbalanced, where the number of data samples in the malignant and benign patient classes are significantly different. Over-sampling techniques can be used to re-balance the datasets to construct more effective prediction models. Moreover, some related studies have considered feature selection to remove irrelevant features from the datasets for further performance improvement. However, since the order of combining feature selection and over-sampling can result in different training sets to construct the prediction model, it is unknown which order performs better. In this paper, the information gain (IG) and genetic algorithm (GA) feature selection methods and the synthetic minority over-sampling technique (SMOTE) are used for different combinations. The experimental results based on two breast cancer datasets show that the combination of feature selection and over-sampling outperform the single usage of either feature selection and over-sampling for the highly class imbalanced datasets. In particular, performing IG first and SMOTE second is the better choice. For other datasets with a small class imbalance ratio and a smaller number of features, performing SMOTE is enough to construct an effective prediction model.

scholarly journals Breast cancer identification based on artificial intelligent system

2020 ◽  
Vol 2 (2) ◽  
pp. 109-118
Author(s):  
Hassan Khalil Silman ◽  
Akbas Ezaldeen Ali
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Intelligent System ◽  
High Rate ◽  
Computer Assisted ◽  
Fine Needle Aspirates ◽  
Manual Intervention ◽  
Breast Tissue Density ◽  
Breast Tissues ◽  
Background Area

Worldwide, breast cancer causes a high mortality rate. Early diagnosis is important for treatment, but high-density breast tissues are difficult to analyze. Computer-assisted identification systems were introduced to classify by fine needle aspirates FNA with features that better represent the images to be classified as a major challenge. This work is fully automated, and it does not require any manual intervention from user. In this analysis, various texture definitions for the portrayal of breast tissue density on mammograms are examined within addition to contrasting them with other techniques. We have created an algorithm that can be divided into three classes: fatty, fatty-glandular and dense-glandular. The suggested system works in a spatial-related domain and it results with extreme immunity to noise and background area, with a high rate of precision.

Breast cancer identification based on artificial intelligent system

2020 ◽  
Vol 2 (2) ◽  
pp. 41-49
Author(s):  
Hassan Khalil Silman ◽  
Akbas Ezaldeen Ali
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Intelligent System ◽  
High Rate ◽  
Computer Assisted ◽  
Fine Needle Aspirates ◽  
Manual Intervention ◽  
Breast Tissue Density ◽  
Breast Tissues ◽  
Background Area

Worldwide, breast cancer causes a high mortality rate. Early diagnosis is important for treatment, but high density breast tissues are difficult to analyze. Computer-assisted identification systems were introduced to classify is fine needle aspirates (fna) , with features that better represent the images to be classified as a major challenge. This work is fully automated, and it does not require any manual intervention from user. In this analysis, various texture definitions for the portrayal of breast tissue density on mammograms are examined within addition to contrasting them with other techniques. We have created an algorithm that can be divided into three classes: fatty, fatty-glandular and dense-glandular, The suggested system works in a spatial-related domain and it results extremely immunity to noise and background area, with a high rate of precision.

Reevaluation of annexin I expression in benign breast tissue and breast cancer

2006 ◽  
Vol 66 (S 01) ◽  
Author(s):  
R Speer ◽  
JD Wulfkuhle ◽  
D Wallwiener ◽  
E Solomayer ◽  
LA Liotta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Annexin I ◽  
Benign Breast Tissue

Copper Concentrations in Breast Cancer: A Systematic Review and Meta-Analysis

2020 ◽  
Vol 27 (37) ◽  
pp. 6373-6383 ◽  
Author(s):  
Leila Jouybari ◽  
Faezeh Kiani ◽  
Farhad Islami ◽  
Akram Sanagoo ◽  
Fatemeh Sayehmiri ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Breast Tissue ◽  
Reference Lists ◽  
Meta Analysis ◽  
Cancer Risks ◽  
Hair Samples ◽  
Significant Difference ◽  
Mean Differences ◽  
Very High

: Breast cancer is the most common neoplasm, comprising 16% of all women's cancers worldwide. Research of Copper (Cu) concentrations in various body specimens have suggested an association between Cu levels and breast cancer risks. This systematic review and meta-analysis summarize the results of published studies and examine this association. We searched the databases PubMed, Scopus, Web of Science, and Google Scholar and the reference lists of relevant publications. The Standardized Mean Differences (SMDs) between Cu levels in cancer cases and controls and corresponding Confidence Intervals (CIs), as well as I2 statistics, were calculated to examine heterogeneity. Following the specimens used in the original studies, the Cu concentrations were examined in three subgroups: serum or plasma, breast tissue, and scalp hair. We identified 1711 relevant studies published from 1984 to 2017. There was no statistically significant difference between breast cancer cases and controls for Cu levels assayed in any studied specimen; the SMD (95% CI) was -0.01 (-1.06 - 1.03; P = 0.98) for blood or serum, 0.51 (-0.70 - 1.73; P = 0.41) for breast tissue, and -0.88 (-3.42 - 1.65; P = 0.50) for hair samples. However, the heterogeneity between studies was very high (P < 0.001) in all subgroups. We did not find evidence for publication bias (P = 0.91). The results of this meta-analysis do not support an association between Cu levels and breast cancer. However, due to high heterogeneity in the results of original studies, this conclusion needs to be confirmed by well-designed prospective studies.

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