Breast cancer identification based on artificial intelligent system

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.

Breast cancer identification based on artificial intelligent system

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.

A Digital Mammogram Auto Classification Method Based on Fibroglandular Breast Tissue Density Evaluation by Image Similarity

It is crucial to assess the fibroglandular breast tissue density to define the degree of the risk that the healthy breast tissue will obscure the lesions. Subjective assessment criteria, proceed by the reading physicians by using the mammary gland concentrations on mammograms, are defined as the breast classification method. However, due to the existence of between observer’s variability, a computer-based quantitative classification method is required. The conventional method classifies according to the ratio of the Dmg region (mammary gland region) to the Dc region (fibroglandular breast tissue region). However, this does not include subjective evaluation elements. The purpose of this study is to improve the concordance rate with the subjective assessment by performing an automated classification based on image similarity. First, 130 cases of right MLO (Medio-Lateral Oblique) images, subjectively classified as fatty tissue, mammary gland diffuseness, non-uniform high density, and high density, were reclassified to two groups; fatty tissue and mammary gland diffuseness as Non-Dense breast, and non-uniform high density and high density as Dense breast. Next, as for evaluation images, 33 cases of both sides MLO images taken by different mammography devices were used. Finally, the image similarity analysis result using Normalized Cross-Correlation between the search image and the evaluation image was derived, and the degree of coincidence of subjective breast classification was calculated. As a result, the concordance rate between the conventional method and the subjective evaluation results of this method improved from 73 % to 91 %, and the kappa coefficient improved from 0.49 to 0.81. This result indicates that our approach is more useful for the automated classification of mammograms based on fibroglandular breast tissue density.

Outcomes after Surgery for Early Stage Breast Cancer in Women Staged With Preoperative Breast Magnetic Resonance Imaging According to Breast Tissue Density

Purpose To determine surgical outcomes and breast cancer disease-free survival outcomes of women with early stage breast cancer with and without use of preoperative breast MRI according to breast tissue density. Methods Women with early stage breast cancer diagnosed from 2004 to 2009 were classified into 2 groups: 1) those with dense and heterogeneously dense breasts (DB); 2) those with nondense breasts (NDB) (scattered fibroglandular and fatty replaced tissue). The 2 groups were reviewed to determine who underwent preoperative MRI. Breast tissue density was determined with mammography according to ACR BI-RADS. Patients were compared according to tumor size, grade, stage, and treatment. Survival analysis was performed using Kaplan-Meier estimates. Results In total, 261 patients with mean follow-up of 85 months (25–133) were included: 156 DB and 105 NDB. Disease-free survival outcomes were better in the DB group in patients with MRI than in those without MRI: patients with MRI had significantly fewer local recurrences (P < 0.016) and metachronous contralateral breast cancers (P < 0.001), but this was not the case in the NDB group. Mastectomies were higher in the DB group with preoperative MRI than in those without MRI (P < 0.01), as it was in the NDB group (P > 0.05). Conclusions Preoperative breast MRI was associated with reduced local recurrence and metachronous contralateral cancers in the DB group, but not in the NDB group; however, the DB patients with MRI had higher mastectomy rates.

Assessment and Classification of Mass Lesions Based on Expert Knowledge Using Mammographic Analysis

Background: Masses are one of the most important indicators of breast cancer in mammograms, and their classification into two groups as benign and malignant is highly necessary. Computer Aided Diagnosis (CADx) helps radiologists enhance the accuracy of their decision. Hence, the system is required to support and assess with radiologist's interaction as an expert. Methods: In this research, classification of breast masses using mammography in the two main views which include MLO and CC, is evaluated with respect to the shape, texture and asymmetry aspect. Additionally, a method was developed and proposed using the classification of breast tissue density based on the decision tree. </P><P> Discussion: This study therefore, aims to provide a method based on the human decision-making model that will help in designing the perfect tool for radiologists, regardless of the complexity of computing, costly procedures and also reducing the diagnosis error. Conclusion: Results show that the proposed system for entirely fat, scattered fibroglandular densities, heterogeneously dense, and extremely dense breast achieved 100, 99, 99 and 98% true malignant rate, respectively with cross-validation procedure.