Cancer Care Facilities Nurses Experience of Infection Control

Hyeon, Yong Hwan; Moon, Kyoung Ja

doi:10.7739/jkafn.2020.27.1.12

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…The data calculated by the edge detection algorithm are binarized and reshaped into a 2D array with elements 0 or 1 when scanned with the 2 × 2 area. The array then undergoes convolution using the filter [ [1,2], [4,8]]. The convoluted area is replaced by the filter's parameter value corresponding to the parameter one position within the scanned area.…”

Section: Discussionmentioning

confidence: 99%

“…During the conversion, visual data can be expressed in a decimal form. For example, if has the parameters [ [1,0], [1,1]], the internal parameters of are changed into [ [1,0], [4,8]] using , and the changed results are added to generate "13." If this process is applied, the calculated value depends on the state of the internal parameters.…”

Section: B Elfa Map Generation Through the Edge Information Based Line Segment Classificationmentioning

confidence: 99%

“…The danger signals of each of these are varied. The number of cancer cases has increased from 207,980 in 2010 to 229,180 in 2016, and the death rate has exhibited an increasing trend [1], [2]. Among various types of cancers, breast cancer is known to occur in numerous developed countries.…”

Section: Introductionmentioning

confidence: 99%

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Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning Model

2020

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Breast cancer is known to be common in many developed countries. It is reported as the most common type of cancer in the US, affecting one in eight women. In Korea, thyroid cancer is the most common type of cancer, followed by breast cancer in women. Considering this, early detection and accurate diagnosis of breast cancer are crucial for reducing the associated death rate. Recently, cancer diagnosis systems using medical images have attracted significant attention. Medical imaging methods, such as computed tomography and magnetic resonance imaging, can reveal the overall shape, heterogeneity, and growth speed of carcinoma and are, thus, more commonly employed for diagnoses. Medical imaging has gained popularity since a recent study identified that it could reflect the gene phenotype of a patient. However, an aided diagnosis system based on medical images requires high-specification equipment to analyze high-resolution data. Therefore, this paper proposes an edge extraction algorithm and a modified convolutional recurrent neural network (CRNN) model to accurately assess breast cancer based on medical imaging. The proposed algorithm extracts line-segment information from a breast mass image. The extracted line segments were classified into 16 types. Each type was uniquely labeled and compressed. The image compressed in this process was used as the input for the modified CRNN model. Traditional deep learning models were used to evaluate the performance of the proposed algorithm. The results show that the proposed model had the highest accuracy and lowest loss (99.75% and 0.0257, respectively).

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Section: Discussionmentioning

confidence: 99%

Section: B Elfa Map Generation Through the Edge Information Based Line Segment Classificationmentioning

confidence: 99%

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Breast Mass Classification Using eLFA Algorithm Based on CRNN Deep Learning Model

2020

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“…Cancer is classified into various types, depending on where the tumor develops. Breast cancer is one of the most common diseases in American women, and in 2019, 41,760 out of about 268,600 breast cancer patients died [1][2][3]. About 80% of breast cancer patients die within 5 years as the tumor metastasizes to other organs regardless of treatment.…”

Section: Introductionmentioning

confidence: 99%

Health Risk Detection and Classification Model Using Multi-Model-Based Image Channel Expansion and Visual Pattern Standardization

et al. 2021

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Although mammography is an effective screening method for early detection of breast cancer, it is also difficult for experts to use since it requires a high level of sensitivity and expertise. A computer-aided detection system was introduced to improve the detection accuracy of breast cancer in mammography, which is difficult to read. In addition, research to find lesions in mammography images using artificial intelligence has been actively conducted in recent days. However, the images generally used for breast cancer diagnosis are high-resolution and thus require high-spec equipment and a significant amount of time and money to learn and recognize the images and process calculations. This can lower the accuracy of the diagnosis since it depends on the performance of the equipment. To solve this problem, this paper will propose a health risk detection and classification model using multi-model-based image channel expansion and visual pattern shaping. The proposed method expands the channels of breast ultrasound images and detects tumors quickly and accurately through the YOLO model. In order to reduce the amount of computation to enable rapid diagnosis of the detected tumors, the model reduces the dimensions of the data by normalizing the visual information and use them as an input for the RNN model to diagnose breast cancer. When the channels were expanded through the proposed brightness smoothing and visual pattern shaping, the accuracy was the highest at 94.9%. Based on the images generated, the study evaluated the breast cancer diagnosis performance. The results showed that the accuracy of the proposed model was 97.3%, CRNN 95.2%, VGG 93.6%, AlexNet 62.9%, and GoogleNet 75.3%, confirming that the proposed model had the best performance.

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