• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.4 s.107
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• pp.341-350
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• 2006

In this paper, a new method for detecting breast cancer is proposed, which utilizes dielectric characteristics of pathological tissues and time delay of back scattered response, and its feasibility was investigated. We have developed a detection algorithm and verified it by numerical simulation and measurement for a prototype system. For a prototype system, we have fabricated experimental model(artificial breast with a cancer) and UWB(ultra-wideband) antenna. The results of the measurement simulation show an excellent detection capability of a cancer tissue. It is found that a good UWB antenna and a good calibration signal are key elements of such detection system. Further study is ongoing to develop a commercial system.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.482-491
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• 2005

In this paper, we proposed the microcalcification detection algorithm which is based on wavelet transform and automatic thresholding method in the X-ray mammographic images. Digital X-ray imaging system is essential equipment in the field diagnosis and is widely used in the various fields such as chest, fracture of a bone, and dental correction. Especially, digital X-ray mammographic imaging is known as the most important method to diagnose the breast cancer, many researches to develop the imaging system are processing in country. In this paper, we proposed a microcalcifications detection algorithm necessary in the early phase of breast cancer diagnosis and showed that a algorithm could effectively detect microcalfication and could aid diagnosis-radiologist.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.12
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• pp.1124-1131
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• 2011

In this paper, image reconstruction algorithm for breast cancer detection using MT(Microwave Tomography) was investigated. The breast cancer detection system under development uses 16 transmit/receive antennas. The signal waveform was a sinusoidal wave at 900 MHz. To solve the 2D inverse scattering problem, we used the 2D FDTD (Finite Difference Time Domain) method for forward calculation and LMA(Levenberg-Marquardt Algorithm) for optimization. The result of the image reconstruction using the numerical phantom by MRI(Magnetic Resonance Imaging) obtained from real patient of breast cancer showed that we can detect the position of the tumor accurately.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.70-77
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• 2019

Breast ultrasound readings are very important to diagnose early breast cancer. In Ultrasonic inspection, it shows a significant difference in image quality depending on the ultrasonic equipment, and there is a large difference in diagnosis depending on the experience and skill of the inspector. Therefore, objective criteria are needed for accurate diagnosis and treatment. In this study, we analyzed texture characteristics by applying GLCM (Gray Level Co-occurrence Matrix) algorithm and extracted characteristic parameters and diagnosed breast cancer using neural network classifier. Breast ultrasound images were classified into normal, benign and malignant tumors and six texture parameters were extracted. Fourteen cases of normal, malignant and benign tumor diagnosed by mammography were studied by using the extracted six parameters and learning by multi - layer perceptron neural network back propagation learning method. As a result of classification using 51 normal images, 62 benign tumor images, and 74 malignant tumor images of the learned model, the classification rate was 95.2%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.562-570
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• 2019

The purpose of this study was to identify the factors influencing the quality of sleep in breast cancer patients receiving chemotherapy in the outpatient settings. The data were collected from 203 patients with breast cancer receiving chemotherapy in the outpatient settings at one tertiary hospital in B City. Stress, fatigue and depression were negatively correlated with quality of sleep (r=-.369, p=.001; r=-.565, p=.001; r=-.526, p=.001, respectively). Fatigue(${\beta}=-.387$, p<.001) was one of the biggest impact factors on quality of sleep which explained 31.6% of the variance of the sleep quality, followed by the experience of sleep disturbances prior to the diagnosis of breast cancer(${\beta}=-.178$, p<.002) and depression(${\beta}=-.231$, p<.004). In total, all of the antecedent variables explained significantly 37.4% of the variance of the sleep quality. Thus, in order to improve the quality of sleep, integrative nursing interventions need to be developed to reduce fatigue and depression among them, including an proactive system to screen out the patients with the experience of sleep disturbances prior to the diagnosis with breast cancer and to provide adequate pharmacological and/or non-pharmacological sleep interventions prior to the chemotherapy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.134-136
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• 2021

Research related to early diagnosis of breast cancer using artificial intelligence algorithms has been actively conducted in recent years. Although various algorithms that classify breast cancer based on a few publicly available ultrasound breast cancer images have been published, these methods show various limitations such as, processing speed and accuracy suitable for the user's purpose. To solve this problem, in this paper, we propose a multi-stage transfer learning where ResNet model trained on ImageNet is transfer learned to microscopic cancer cell line images, which was again transfer learned to classify ultrasound breast cancer images as benign and malignant. The images for the experiment consisted of 250 breast cancer ultrasound images including benign and malignant images and 27,200 cancer cell line images. The proposed multi-stage transfer learning algorithm showed more than 96% accuracy when classifying ultrasound breast cancer images, and is expected to show higher utilization and accuracy through the addition of more cancer cell lines and real-time image processing in the future.

• The Science & Technology
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• v.34 no.9 s.388
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• pp.88-89
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• 2001

• 전기의세계
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• v.61 no.6
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• pp.30-33
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• 2012

• The Korean Chronic Disease News
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• no.146
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• pp.15-15
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• 1993

• The Journal of the Korean life insurance medical association
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• v.19
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• pp.109-117
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• 2000

연구배경 : 3대 특정질병 진단보험금지급의 양상과 경향을 평가하고자 하였다. 방법 : 1997년 7월${\sim}$1999년 3월까지 당사의 한 건강보험가입자 중 1998년 1월${\sim}$1999년 9월 기간동안 당사 약관상의 정의에 의한 악성종양, 급성심근경색증, 뇌졸중으로 진단보험금이 지불된 총 411건에 대해 조사하였다. 결과 : 3대 특정질병 진단보험금 지급건 총 411건의 구성을 보면 악성종양이 290건(70.6%), 급성심근경색이 25건(6.1%) 그리고 뇌졸중이 96건(23.3%)이었다. 남녀비율은 남자 280건(68.1%), 여자 131건(31.9%)이었다. 3대 특정질병 진단급여금 지급건의 평균연령은 $3.88{\pm}5.9$이었다. 3대 특정질병 진단보험금 지불건은 $30{\sim}39$세 연령대에서 187건(45.4%)으로 가장 많았고, 그 다음으로 $40{\sim}49$세 연령대 178건(43.2%)의 순이었다. 계약시점에서 3대 특정질병 진단보험금 지급 시까지 평균진단확정 기간은 325.2일${\pm}$184.9일 이었다. 계약 후 12개월 내에 진단지급보험금 발생건은 총 193건(55.3%)이었고, 12개월 이후에 지급된 건은 156건(44.7%)이었다. 계약 후 12개월 내에 진단지금보험금 발생건 193건을 분석하여 보면 3개월 이상${\sim}$4개월 미만이 40건(20.7%)로 가장 많았다. 악성종양의 신체계통별로 보면 소화기관>유방>여자생식기>호흡기계 순이었다. 악성종양을 장기별로 보면 위암>유방암>간암 및 담도계암>결장암과 직장암, 자궁경부암의 순이었다. 남자의 경우 위암>간암 및 담도계암>결장암과 직장암의 순이었고 여자의 경우 유방암>자궁경부암(상피내암 제외)>결장암, 직장암의 순이었다. 뇌졸중의 종류별 빈도를 보면 뇌경색증(47.9%)>뇌내출혈(34.4%)>거미막하출혈(9.4%)의 순이었다. 결론 : 3대 특정질병 중 악성종양이 다수를 차지하고 있었고, 남자가 여자보다 훨씬 많았고 주로 $30{\sim}39$세 연령대, $40{\sim}49$세 연령대였다. 계약 후 12개월 내에 진단지급보험금 발생건을 분석하여 보면 3개월 이상${\sim}$4개월 미만이 40건(20.7%)으로 가장 많았다는 것은 역선택의 가능성 그리고 제척기간 중 발생한 3대 특정질환이 3개월 이후 특히 3개월 이상${\sim}$4개월 미만 사이에 지급청구되었을 가능성을 시사하는 것으로 사료된다.