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A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

Oza, P., Sharma, P., Patel, S. and Bruno, A., 2021. A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms. Journal of Imaging, 7 (9), 190.

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DOI: 10.3390/jimaging7090190

Abstract

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

Item Type:Article
ISSN:2313-433X
Additional Information:This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis
Uncontrolled Keywords:deep learning ; low-level features ; machine learning ; mammograms
Group:Faculty of Science & Technology
ID Code:36072
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:04 Oct 2021 15:32
Last Modified:04 Oct 2021 15:32

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