• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1277-1284
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• 2016

Background: Early detection is a critical part of reducing the burden of breast cancer and breast self-examination (BSE) has been found to be an especially important early detection strategy in low and middle income countries such as Malaysia. Although reports indicate that Malaysian women report an increase in BSE activity in recent years, additional research is needed to explore factors that may help to increase this behavior among Southeastern Asian women. Objective: This study is the first of its kind to explore how the predicting variables of self-efficacy, perceived barriers, and body image factors correlate with self-reports of past BSE, and intention to conduct future breast self-exams among female students in Malaysia. Materials and Methods: Through the analysis of data collected from a prior study of female students from nine Malaysian universities (n=842), this study found that self-efficacy, perceived barriers and specific body image sub-constructs (MBSRQ-Appearance Scales) were correlated with, and at times predicted, both the likelihood of past BSE and the intention to conduct breast self-exams in the future. Results: Self-efficacy (SE) positively predicted the likelihood of past self-exam behavior, and intention to conduct future breast self-exams. Perceived barriers (BR) negatively predicted past behavior and future intention of breast self-exams. The body image sub-constructs of appearance evaluation (AE) and overweight preoccupation (OWP) predicted the likelihood of past behavior but did not predict intention for future behavior. Appearance orientation (AO) had a somewhat opposite effect: AO did not correlate with or predict past behavior but did correlate with intention to conduct breast self-exams in the future. The body image sub-constructs of body area satisfaction (BASS) and self-classified weight (SCW) showed no correlation with the subjects' past breast self-exam behavior nor with their intention to conduct breast self-exams in the future. Conclusions: Findings from this study indicate that both self-efficacy and perceived barriers to BSE are significant psychosocial factors that influence BSE behavior. These results suggest that health promotion interventions that help enhance self-efficacy and reduce perceived barriers have the potential to increase the intentions of Malaysian women to perform breast self-exams, which can promote early detection of breast cancers. Future research should evaluate targeted communication interventions for addressing self-efficacy and perceived barriers to breast self-exams with at-risk Malaysian women. and further explore the relationship between BSE and body image.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.6
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• pp.2917-2921
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• 2016

Background: To explore factors that influence quality of life (QOL) in patients receiving breast conserving therapy (BCT). Materials and Methods: In this sequential mixed methods study, 118 women from Songklanagarind Hospital were included. We used participants' characteristics, Body Image Scale (BIS), and Functional Assessment of Cancer Therapy with the Breast Cancer Subscale (FACT-B) for analysis. The BIS transformed into presence of body image disturbance (BID). Factors that influenced QOL were determined by stepwise multiple linear regression. Forty-one participants were selected for qualitative analysis. Our female researcher performed the semi-structured interviews with questions based on the symbolic interaction theory. Final codes were analysed using thematic analysis along with investigator triangulation methods. Results: Ninety percent had early stage breast cancer with post-completed BCT, for an average of 2.7 years. The median BIS score and FACT-B score were 2 (IQR=10) and 130 (IQR=39). In the regression analysis, an age of more than 50 years and BID were significant factors. As for the value of conserved breasts, two themes emerged: a conserved breast is an essential part of a participant's life and also the representation of her womanhood; the importance of a breast is related to age. Conclusions: Body image influenced QOL in post BCT participants. The conserved breasts also lead to positive and better impact on their body image as an essential part of their life.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.2
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• pp.270-276
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• 2003

Methodology was suggested to analyze breast volume, base area of breast bulk. and surface area of breast using the 3D measurement system. Thirty-seven middle-aged (30s-40s) women wearing 80A brassiere were participated in this study. Image of the upper body was captured by Phase-shifting moire. The posture of the subject was adjusted to get the full image of the right breast. Rapidform 2001 was used for the analysis of the images. The mean breast volume was 547.0㎤ and mean base area of breast bulk was 235. I$\textrm{cm}^2$ It was also found that the volume(r=0.169) and surface area of breast(r=10.242) were loosely correlated with the circumference difference between top and under breast. Therefore, it is noted that current selection criterion of cup size based on the difference in the two kinds of breast circumference is inadequate. The result of this study is expected to contribute to the design of ergonomic brassiere as well as surgical operations in the medical field.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1363-1369
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• 2018

Breast cancer is one of the most common cancers in women worldwide. In Korea, breast cancer is most common cancer in women followed by thyroid cancer. The purpose of this study is to evaluate the possibility of using deep - run model for segmentation of breast masses and to identify the best deep-run model for breast mass segmentation. In this study, data of patients with breast masses were collected at Asan Medical Center. We used 596 images of mammography and 596 images of gold standard. In the area of interest of the medical image, it was cut into a rectangular shape with a margin of about 10% up and down, and then converted into an 8-bit image by adjusting the window width and level. Also, the size of the image was resampled to $150{\times}150$. In Deconvolution net, the average accuracy is 91.78%. In U-net, the average accuracy is 90.09%. Deconvolution net showed slightly better performance than U-net in this study, so it is expected that deconvolution net will be better for breast mass segmentation. However, because of few cases, there are a few images that are not accurately segmented. Therefore, more research is needed with various training data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.655-657
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• 2021

It is challenging to find breast ultrasound image training dataset to develop an accurate machine learning model due to various regulations, personal information issues, and expensiveness of acquiring the images. However, studies targeting transfer learning for ultrasound breast cancer images classification have not been able to achieve high performance compared to radiologists. Here, we propose an improved transfer learning model for ultrasound breast cancer classification using publicly available dataset. We argue that with a proper combination of ImageNet pre-trained model and optimizer, a better performing model for ultrasound breast cancer image classification can be achieved. The proposed model provided a preliminary test accuracy of 99.5%. With more experiments involving various hyperparameters, the model is expected to achieve higher performance when subjected to new instances.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.18 no.3
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• pp.328-336
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• 2011

Purpose: The purpose of this study was to identify the effects of cancer-overcome BeHaS exercise program on shoulder joint function, stress, body image and self-esteem in women who have had surgery for breast cancer. Method: A non-equivalent control group pre-post test design with an experimental group (n=25) and a control group (n=25) was used. The experimental group participated in the program once a week for eight weeks. Data were analyzed using descriptive statistics and Chi square and t-test with the SPSS Win 17.0. Results: There were significantly increased in shoulder joint function (p=.012), body image (p=.001), and self-esteem (p=.013), and significantly decreased in stress (p=.003). Conclusion: The results suggest that breast cancer-overcome BeHaS exercise program had beneficial effects on shoulder joint function, body image, self-esteem and stress in patients who have had surgery for breast cancer.

• Research in Community and Public Health Nursing
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• v.23 no.4
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• pp.405-414
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• 2012

Purpose: The purpose of this study was to examine the sexual maturation, body image, and self-esteem of Korean elementary school girls with symptoms of precocious puberty compared to those with no symptoms of precocious puberty. Methods: The subjects were 309 girls of lower grades in elementary school. Tanner's Sexual Maturation Rating (SMR), Self Image Scale, and Self-esteem Scale were utilized to determine the presence of symptoms of precocious puberty, body image, and self esteem. Collected data were analyzed by Chi-square test, independent t-test, and one-way ANOVA using the SPSS/WIN 17.0 program. Results: The percentage of the girls with breast development was 14.9%. Breast development usually began in the third grade (56.5%). Of the subjects, 0.3% were experiencing menstruation. Breast development was related to grade, age, height, weight, and a cause of worry. There was a significant difference of body image between girls with breast development in the first grade and in the second grade. Conclusion: An effective intervention that can improve the self-image of children with symptoms of precocious puberty should be developed to prevent and treat physical and mental problems related to sexual maturation.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.175-181
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• 2017

The purpose of this study is to evaluate the breast tool to improve the diagnostic value of the image in the breast examination. Breast tool was made of using FRP. And then it was compared by radioactivity counting rate and image. In the evaluation of the Breast tool, the left and right counts per $1{\mu}Ci$ are 185 counts and 189 counts, respectively. The image obtained in the prone position was close to the circle. To increase diagnostic value of image, it is considered to use Breast-tool in the breast examination.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10573-10576
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• 2015

Background: Accuracy in feature extraction is an important factor in image classification and retrieval. In this paper, a breast tissue density classification and image retrieval model is introduced for breast cancer detection based on thermographic images. The new method of thermographic image analysis for automated detection of high tumor risk areas, based on two-directional two-dimensional principal component analysis technique for feature extraction, and a support vector machine for thermographic image retrieval was tested on 400 images. The sensitivity and specificity of the model are 100% and 98%, respectively.