Breast Cancer Classification Enhancement Based on Entropy Method

Sama, Ali Salem Ali Bin; Baneamoon, Saeed Mohammed

doi:10.24032/ijeacs/0208/06

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Earlier breast cancer can detect will make the better chance to recover. Various radiological examination modalities are used in detecting breast cancer [5][6] [7]. The most frequently used types of radiology are the use of X-rays (mammography), ultrasound, and MRI [5] [6].…”

Section: Introductionmentioning

confidence: 99%

“…But this research is only using cropping method in ROI of the cancer area, so this method can't use in MLO view image without cropping. Furthermore, [7] developed a research method to detect and classify breast cancer using an entropy-based method which can achieve significant results when compared to other model methods. [13] describes the method of breast cancer detection on mammographic images using the entropy method with addition information about the radius and area of cancer tissue detected.…”

Section: Introductionmentioning

confidence: 99%

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Using Pectoral Muscle Removers in Mammographic Image Process to Improve Accuracy in Breast Cancer

Firdi

Sardjono

Hikmah

2022

JBBBE

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Cancer is a disease that attacks almost any organ or tissue of the body when abnormal cells grow uncontrollably and invade adjacent parts of the body. The second highest incidence of cancer in Indonesia is breast cancer with 42.1 cases per 100,000 population with an average mortality rate of 17 per 100,000 population. Mammography is a special imaging modality with x-rays to produce detailed breast images with at least 2 viewpoints, namely Craniocaudal (CC) or top view and Medio Lateral Oblique (MLO) or side view. The chest muscle area on the MLO display often interferes with the cancer identification process on mammography images because it has a dominant density and is similar to the density of cancer tissue. This research proposes a framework consisting of pectoral muscle detection on MLO display, image enhancement process, segmentation, and feature extraction. This study succeeded in increasing the accuracy of the MLO display mammography image after using the pectoral muscle remover using gradual edge detection and Hough lines Transform with the ratios of accuracy, precision, specificity, and sensitivity for images without pectoral muscle removers respectively were 33.59%, 30%, 11.49% and 80.48%. As for the images with pectoral muscle removers, the accuracy, precision, specificity, and sensitivity values respectively were 68.67%, 64.71%, 57.14%, and 80.49%. For future projects, this research can be developed using Convolutional Neural Network (CNN) to improve accuracy. This is expected to help doctors and radiologists in the process of reading patient mammography so it can reduce mortality from breast cancer.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Pectoral Muscle Removers in Mammographic Image Process to Improve Accuracy in Breast Cancer

Firdi

Sardjono

Hikmah

2022

JBBBE

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Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms

et al. 2022

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An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images

Hikmah

Sardjono

Mertiana

et al. 2022

EMITTER Int'l J. of Engin. Technol.

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Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment.

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Breast Cancer Classification Enhancement Based on Entropy Method

Cited by 3 publications

References 7 publications

Using Pectoral Muscle Removers in Mammographic Image Process to Improve Accuracy in Breast Cancer

Using Pectoral Muscle Removers in Mammographic Image Process to Improve Accuracy in Breast Cancer

Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms

An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images

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