• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.3
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• pp.385-392
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• 2010

The breast types of middle-aged women of 80A (formerly 80B) size were classified through a 3D scanned nude body. Thirty seven measurements including the radius of curvature, surface area, volume, surface length, and breast displacements were used as input variables. We extracted five main factors through the factor analysis of the measurements and classified 36 subjects into 3 clusters through the cluster analysis. As a result of the factor analysis, the size of the breast, breast sag, the curvature of the inner and the outer breast curve, the width of the breast, and the nipple direction were found as the main factors. For the results of the classification of breast types, Cluster 1 was characterized by narrow breast width and unsymmetrical under the breast curve, whereas Cluster 2 was a wide and sagged shape. Cluster 3 was classified into big breast volume and symmetrical under-breast curve. The results are useful to the product development of high quality brassieres which reflect the 3D characteristics of breast types of middle-aged women.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.137-146
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• 2020

The manual measurements of 182 unmarried women subjects in their 20s was classified 4-breast types. For the breast type classification, 4 factors were identified, such as overall breast factor, upper breast internal shape factor, breast volume factor, and lower breast external shape factor. The breast shapes were 'breast with well-grown upper part', 'flat breast', 'breast with well-grown lower part', and 'protruded breast'. The breast types can be differentiated in 10 items of actual anthropometric dimension the length between frontal neck point and nipple point, length between lateral neck point and nipple point, length between the breast inner points, nipple to nipple breadth, diameter below the breast, inner depth of breast, outer length of breast, length below the breast, length between breast outer point and upper breast point, and contour line length below the breast.

• Korean Journal of Human Ecology
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• v.18 no.6
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• pp.1267-1276
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• 2009

The purpose of this study is to classify breast types and to inquire about characteristics depending on breast types of women subjects in their twenties. We researched size items affecting breast volume and regression equations for the prediction of breast volume, and thereby, we will be able to provide some basic data, useful to the development of the brassiere depending on breast types. As a result of categorizing the types of three breast types, "type 1" was characterized by big and greatest protrusion of the breast with large breast volume and a large bust, while "type 2" was characterized by flat breasts with the least breast volume and least bust, and "type 3" was characterized by breast location apart from the center front line. Breast volume is significant in establishment of the brassiere cup depending on breast type. Five items such as, the circumference of the breast, the length of the upper breast, the depth of the breast point, the length of the shoulder point-breast point, and the length of the inferior breast were extracted through regression equations for breast volume.

• Genomics & Informatics
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• v.15 no.4
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• pp.156-161
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• 2017

Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.

• Journal of Fashion Business
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• v.21 no.2
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• pp.61-77
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• 2017

Although three-dimensional measurement systems for the human body have been studied, there is still an error between the measurements by the two-dimensional measurement method and the three-dimensional scanning method. Especially, in the case of the breast, the outline is not clear. The breast is made up of subcutaneous fat and mammary gland tissue, and it is easy to deform, making it difficult to grasp the exact shape. It is also more difficult to measure photogrammetry or three-dimensional measurement because it is difficult to obtain subjects because of the shame they are reluctant to expose. In this study, the angle and length of the line connecting the measurement points of the breast detail measurement items were compared with the unchanged measurement items such as breast width and center front length using the frontal and lateral photographs taken before and after breast enlargement surgery. The results of the study are as follows. The types of breast before and after surgery were classified into two groups and showed high accuracy rate. Therefore, it was possible to classify the breast type using the frontal and lateral views of the breast, and it was found that The PRM method can distinguish the characteristics of the breast type. Therefore, it can be useful for classifying and discriminating breast types.

• Journal of the Korean Home Economics Association
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• v.41 no.6
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• pp.119-129
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• 2003

This study was peformed to establish the standard size system to be required for the production of brassiere. The subject were 155 Korean twenties-aged women and were directly measured anthropometrically. From 27 measurements, 5 factors were extracted through factor analysis. The accumulative contribution ratio is 76.92％. Factor 1 indicates the degree of obesity around the chest, factor 2 is the drooping degree of breast, factor 3 is the contours and prominence, factor 4 is the breast breadth and breadth of bust point, factors 5 is the volume of breast. The subject were classified into 3 cluster as their breast types through cluster analysis. Type 1 is the closest to the ideal breast shape and not too droopy. This group belonged to 75A. Type 2 has small breast and belonged to 70AA group. Type 3 is the obesest and has the biggest and droopy breast. This group belonged to 75B. The distribution of size of brassiere had 3 sections from 70 to 80 showing a rate of 81.94% and the sin of the cup had 4 sections from AAA cup to B cup showing a rate of 89.70％. The production ratio of each brassiere size, it was found that the brassiere size of highest production ratio was 75A(16.39％) in type 1,70AA(16.27%) in type 2, and 75B(13.72％) in type 3. This suggests that it is necessary to adjust for the production rate of brassieres.

• Fashion & Textile Research Journal
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• v.19 no.6
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• pp.786-794
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• 2017

The purpose of this study is to analyze and classify breast shape of women aged 11~15 using 3D body scan data. In this study, 250 women's body scans were selected from the 6th Size Korea dataset, and 30 items from each of the scan were measured using RapidForm XOR 3 program. The principal component analysis and cluster analysis were conducted using statistical program SPSS 17.0. The five principal components were identified; breast drooping and breast capacity, size from chest to under bust area, breast protrusion, breast height, and under breast angle & outer distance of breast. As the results of cluster analysis, woman's breast types were classified into four types. The breast type 1 was protrusion type (25.1%) which is considered as the breast maturity stage. The breast type 2 had the most drooped breast covering a large area (20.2%). The breast type 3 had the least prominent breast with a highest nipple point, which was considered as the early breast development stage (38.9%). The breast type 4 had the obesity of the chest and breast circumferences with the slightly prominent and the least drooped breast (15.8%). This study can provide fundamental information to develop sizing system and brassiere pattern for junior girls.

• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.2 s.115
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• pp.1-17
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• 2006

The purpose of this study was to analyze biotope types of urban land-use patterns. Forest areas were considered according to vegetation type and potential for succession. Urban ecosystem structure was analyzed according to land use, land coverage, vegetation structure (actual vegetation, diameter at breast height, layer structure, and revetment). As a results of the classification, the biotopes were divided into 71 types according to the urban ecosystem structure. In the case of the Hanam province, the biotopes were divided into 51 types: 26 forest types; 5 swampy and grass land types; 3 farm land types; 3 types of planted land, and 8 types of urbanization.

• The Korean Journal of Cytopathology
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• v.18 no.2
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• pp.100-111
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• 2007

Fine needle aspiration (FNA) cytology of breast disease is recognized to be highly accurate and cost effective, especially when this is used in combination with clinical examination and imaging as part of a triple approach. A probabilistic/categorical approach is used for the classification of breast FNA specimens. Criteria are defined from the perspective of the likelihood of making a definitive diagnosis of cancer on excision. This approach is an accurate way of classifying breast FNA specimens, and this can be reliably applied regardless of the level of experience of the pathologist for interpreting the case. When a definitive diagnosis of malignancy is made, the next step is to determining the specific histologic types of the malignancy according to their cytological features. In order to make an accurate diagnosis of carcinoma and for correct typing a tumor, an adequate, correctly sampled aspirate without any other artifacts is required.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.