Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms

Cruz-Bernal, Alejandra; Flores-Barranco, Martha Magali; Almanza-Ojeda, Dora-Luz; Ledesma, Sergio; Ibarra-Manzano, Mario Alberto

doi:10.1155/2018/2849567

Cited by 12 publications

(10 citation statements)

References 25 publications

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“…Cruz-Bernal et al [ 28 ] recommended a new technique for the identification of calcifications. The recommended technique was constructed on cluster prominence (cp) feature evaluation on mammograms [ 28 ]. The cluster prominence feature was analyzed deeply in this work.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Chaudhury

Rakhra

Memon

et al. 2021

Computational and Mathematical Methods in Medicine

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Breast cancer is a strong risk factor of cancer amongst women. One in eight women suffers from breast cancer. It is a life-threatening illness and is utterly dreadful. The root cause which is the breast cancer agent is still under research. There are, however, certain potentially dangerous factors like age, genetics, obesity, birth control, cigarettes, and tablets. Breast cancer is often a malignant tumor that begins in the breast cells and eventually spreads to the surrounding tissue. If detected early, the illness may be reversible. The probability of preservation diminishes as the number of measurements increases. Numerous imaging techniques are used to identify breast cancer. This research examines different breast cancer detection strategies via the use of imaging techniques, data mining techniques, and various characteristics, as well as a brief comparative analysis of the existing breast cancer detection system. Breast cancer mortality will be significantly reduced if it is identified and treated early. There are technological difficulties linked to scans and people’s inconsistency with breast cancer. In this study, we introduced a form of breast cancer diagnosis. There are different methods involved to collect and analyze details. In the preprocessing stage, the input data picture is filtered by using a window or by cropping. Segmentation can be performed using k -means algorithm. This study is aimed at identifying the calcifications found in bosom cancer in the last phase. The suggested approach is already implemented in MATLAB, and it produces reliable performance.

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Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Chaudhury

Rakhra

Memon

et al. 2021

Computational and Mathematical Methods in Medicine

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“…Calcifications appear as small spots on mammography and there are two in number: microcalcification and macrocalcification. At present, CAD systems are capable of detecting microcalcifications [29]. Breast mass segmentation, which is identified as true segmentation, directly affects the diagnosis.…”

Section: Table 1: Different Methods By Which Artificial Intelligence ...mentioning

confidence: 99%

Artificial Intelligence in Breast Cancer Screening and Diagnosis

Dileep¹,

Gyani²

2022

Cureus

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Cancer is a disease that continues to plague our modern society. Among all types of cancer, breast cancer is now the most common type of cancer occurring in women worldwide. Various factors, including genetics, lifestyle, and the environment, have contributed to the rise in the prevalence of breast cancer among women of all socioeconomic strata. Therefore, proper screening for early diagnosis and treatment becomes a major factor when fighting the disease. Artificial intelligence (AI) continues to revolutionize various spheres of our lives with its numerous applications. Using AI in the existing screening process makes obtaining results even easier and more convenient. Faster, more accurate results are some of the benefits of AI methods in breast cancer screening. Nonetheless, there are many challenges in the process of the integration of AI that needs to be addressed systematically. The following is a review of the application of AI in breast cancer screening.

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“…The most frequent method is to convert 3D photos to 2D images. It is accomplished by means of slicing, in which the 3D image is sliced into 2D, or by applying the highest intensity projection (MIP) [21,22]. DL is utilized to classify the axillary group of lymph node metastases in addition to lesion categorization [23][24][25].…”

Section: Mri Application Of Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence in Breast Ultrasound: The Emerging Future of Modern Medicine

Mahant¹,

Varma²

2022

Cureus

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In today's world, progressively enormous popularity prevails around artificial intelligence (AI). AI is gaining popularity in the identification of various images. Therefore, it has been widely used in the ultrasound of the breast. Furthermore, AI can perform a quantitative evaluation, which further helps maintain the diagnosis's accuracy. Moreover, breast cancer is the most common cancer in women, posing a severe threat to women's health. Hence, its early detection is usually associated with a patient's prognosis. As a result, using AI in breast cancer screening and detection is highly crucial. The concept of AI in the perspective of breast ultrasound has been highlighted in this brief review article. It tends to focus on early AI, i.e., traditional machine learning and deep learning algorithms. Also, the use of AI in ultrasound and the use of it in mammography, magnetic resonance imaging, nuclear medicine imaging, and classification of breast lesions is broadly explained, along with the challenges faced in bringing AI into daily practice.

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Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms

Cited by 12 publications

References 25 publications

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Artificial Intelligence in Breast Cancer Screening and Diagnosis

Artificial Intelligence in Breast Ultrasound: The Emerging Future of Modern Medicine

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