• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.148-150
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• 2006

In this paper, we propose a new method for detection of mass-type breast cancer in dense mammogram. As the proposed method analyzes texture of the breast tissue using method by fusing Homogeneity and Ranklets, improve problem of traditional method. Homogeneity gives the measure of uniform density, and Ranklets determine orientation selective property at vertical, horizontal and diagonal in mass region. The proposed method is suitable to dense mammogram with tangled normal tissue and cancer tissue. SVM(Support Vector Machine) classifier is used for effective detection of mass-type breast cancer in dense mammogram.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.101-106
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• 2021

Breast cancer is the number one cause of deaths from cancer in women, knowing the type of breast cancer in the early stages can help us to prevent the dangers of the next stage. The performance of the deep learning depends on large number of labeled data, this paper presented convolutional neural network for classification breast cancer from images to benign or malignant. our network contains 11 layers and ends with softmax for the output, the experiments result using public BreakHis dataset, and the proposed methods outperformed the state-of-the-art methods.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.113-120
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• 2023

One of the most prevalent disease among women that leads to death is breast cancer. It can be diagnosed by classifying tumors. There are two different types of tumors i.e: malignant and benign tumors. Physicians need a reliable diagnosis procedure to distinguish between these tumors. However, generally it is very difficult to distinguish tumors even by the experts. Thus, automation of diagnostic system is needed for diagnosing tumors. This paper attempts to improve the accuracy of breast cancer detection by utilizing deep learning convolutional neural network (CNN). Experiments are conducted using Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Compared to existing techniques, the used of CNN shows a better result and achieves 99.66%% in term of accuracy.

• Korean Journal of Radiology
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• v.25 no.8
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• pp.698-705
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• 2024

Ductal carcinoma in situ (DCIS) accounts for approximately 30% of new breast cancer diagnoses. However, our understanding of how normal breast tissue evolves into DCIS and invasive cancers remains insufficient. Further, conclusions regarding the mechanisms of disease progression in terms of histopathology, genetics, and radiology are often conflicting and have implications for treatment planning. Moreover, the increase in DCIS diagnoses since the adoption of organized breast cancer screening programs has raised concerns about overdiagnosis and subsequent overtreatment. Active monitoring, a nonsurgical management strategy for DCIS, avoids surgery in favor of close imaging follow-up to de-escalate therapy and provides more treatment options. However, the two major challenges in active monitoring are identifying occult invasive cancer and patients at risk of invasive cancer progression. Subsequently, four prospective active monitoring trials are ongoing to determine the feasibility of active monitoring and refine the patient eligibility criteria and follow-up intervals. Radiologists play a major role in determining eligibility for active monitoring and reviewing surveillance images for disease progression. Trial results published over the next few years would support a new era of multidisciplinary DCIS care.

• Korean Journal of Radiology
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• v.20 no.5
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• pp.749-758
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• 2019

Objective: To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US). Materials and Methods: B-mode US images were prospectively obtained for 253 breast masses (173 benign, 80 malignant) in 226 consecutive patients. Breast mass US findings were retrospectively analyzed by deep learning-based CAD and four radiologists. In predicting malignancy, the CAD results were dichotomized (possibly benign vs. possibly malignant). The radiologists independently assessed Breast Imaging Reporting and Data System final assessments for two datasets (US images alone or with CAD). For each dataset, the radiologists' final assessments were classified as positive (category 4a or higher) and negative (category 3 or lower). The diagnostic performances of the radiologists for the two datasets (US alone vs. US with CAD) were compared Results: When the CAD results were added to the US images, the radiologists showed significant improvement in specificity (range of all radiologists for US alone vs. US with CAD: 72.8-92.5% vs. 82.1-93.1%; p < 0.001), accuracy (77.9-88.9% vs. 86.2-90.9%; p = 0.038), and positive predictive value (PPV) (60.2-83.3% vs. 70.4-85.2%; p = 0.001). However, there were no significant changes in sensitivity (81.3-88.8% vs. 86.3-95.0%; p = 0.120) and negative predictive value (91.4-93.5% vs. 92.9-97.3%; p = 0.259). Conclusion: Deep learning-based CAD could improve radiologists' diagnostic performance by increasing their specificity, accuracy, and PPV in differentiating between malignant and benign masses on breast US.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10739-10743
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• 2015

The present study aimed at evaluating and comparing the diagnostic performance of B-mode ultrasound (US), elastography score (ES), and strain ratio (SR) for the differentiation of breast lesions. This retrospective study enrolled 431 lesions from 417 in-hospital patients. All patients were examined with both conventional ultrasound and elastography. Two experienced radiologists reviewed ultrasound and elasticity images. The histopathologic result obtained from ultrasound-guided core biopsy or operation excisions were used as the reference standard. Pathologic examination revealed 276 malignant lesions (64%) and 155 benign lesions (36%). A cut-off point of 4.15 (area under the curve, 0.891) allowed significant differentiation of malignant and benign lesions. ROC (receiver-operating characteristic) curves showed a higher value for combination of B-mode ultrasound and elastography for the diagnosis of breast lesions. Conventional ultrasound combined elastography showed high sensitivity, specificity, and accuracy for group II lesions (10mm${\leq}20mm$). Elastography combined with conventional ultrasound show high specificity and accuracy for differentiation of benign and malignant breast lesions. Elastography is particularly important for the diagnosis of BI-RADS 4 and small breast lesions.

• Journal of the Korean Society of Radiology
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• v.8 no.1
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• pp.27-33
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• 2014

Medical imaging has increased rapidly in the increase of interest in health, with the development of computer technology, digitization of medical imaging is rapidly advancing, PACS has been introduced to the medical field. Increase in the production of medical images by these phenomena made increased the workload of radiologist who must read a medical image. in response to the need for secondary diagnosis using a computer, The term of CAD in medical radiology field was introduced. In this study, we have proposed a CAD algorithm for the interpretation of the image obtained by the digital X-ray mammography equipment. The experiments were performed by programmed in Visual C++ for the proposed algorithm. A result of the execution of the CAD algorithm seven sample images, the results of five samples was confirmed in breast cancer and benign tumors, both the images sample was error processing. If you use a program that implements this with the algorithm proposed in this study it is helpful to reading breast images, and it is considered to contribute significantly to the early detection of breast cancer.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.183-194
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• 2009

In this paper, we propose a new method for detecting objectionable images with an active shape model. Our method first learns the shape of breast lines through principle component analysis and alignment as well as the distribution of intensity values of corresponding landmarks, and then extracts breast lines with the learned shape and intensity distribution. To accurately select the initial position of active shape model, we obtain parameters on scale, rotation, and translation. After positioning the initial location of active shape model using scale and rotation information, iterative searches are performed. We can identify adult images by calculating the average of the distance between each landmark and a candidate breast line. The experiment results show that the proposed method can detect adult images effectively by comparing various results.

• Journal of the Korean Society of Radiology
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• v.5 no.3
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• pp.149-154
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• 2011

The purpose of this study was to examine the usefulness of diffusion weighted images in breast MRI by performing a quantitative comparative analysis in patients diagnosed with DCIS. On a 3.0T MR scanner, diffusion weighted images and ADC map images were obtained from 20 patients histologically diagnosed with ductal carcinoma in situ (DCIS). The findings from the quantitative image analysis are the following: The diffusion weighted images showed higher SNR and CNR at the lesion area. In addition, the ADC values were lower at the lesion area.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.25-32
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• 2020

Objective: To comparatively evaluate the scan coverage and diagnostic performance of the two-view scan technique (2-VST) of the automated breast ultrasound system (ABUS) versus the conventional three-view scan technique (3-VST) in women with small breasts. Materials and Methods: Between March 2016 and May 2017, 136 asymptomatic women with small breasts (bra cup size A) suitable for 2-VST were enrolled. Subsequently, 272 breasts were subjected to bilateral whole-breast ultrasound examinations using ABUS and the hand-held ultrasound system (HHUS). During ABUS image acquisition, one breast was scanned with 2-VST, while the other breast was scanned with 3-VST. In each breast, the breast coverage and visibility of the HHUS detected lesions on ABUS were assessed. The sensitivity and specificity of ABUS were compared between 2-VST and 3-VST. Results: Among 136 breasts, eight cases of breast cancer were detected by 2-VST, and 10 cases of breast cancer were detected by 3-VST. The breast coverage was satisfactory in 94.1% and 91.9% of cases under 2-VST and 3-VST, respectively (p = 0.318). All HHUS-detected lesions were visible on the ABUS images regardless of the scan technique. The sensitivities and specificities were similar between 2-VST and 3-VST (100% [8/8] vs. 100% [10/10], and 97.7% [125/128] vs. 95.2% [120/126], respectively), with no significant difference (p > 0.05). Conclusion: 2-VST of ABUS achieved comparable scan coverage and diagnostic performance to that of conventional 3-VST in women with small breasts.