[HTML][HTML] Computer-aided breast cancer detection and classification in mammography: A comprehensive review

K Loizidou, R Elia, C Pitris - Computers in Biology and Medicine, 2023 - Elsevier
Cancer is the second cause of mortality worldwide and it has been identified as a perilous
disease. Breast cancer accounts for∼ 20% of all new cancer cases worldwide, making it a …

[HTML][HTML] Machine learning applications in cancer prognosis and prediction

K Kourou, TP Exarchos, KP Exarchos… - Computational and …, 2015 - Elsevier
Cancer has been characterized as a heterogeneous disease consisting of many different
subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in …

ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging

J Ren - Knowledge-Based Systems, 2012 - Elsevier
Classification of microcalcification clusters from mammograms plays essential roles in
computer-aided diagnosis for early detection of breast cancer, where support vector …

Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer

ML Giger, N Karssemeijer… - Annual review of …, 2013 - annualreviews.org
The role of breast image analysis in radiologists' interpretation tasks in cancer risk
assessment, detection, diagnosis, and treatment continues to expand. Breast image analysis …

CADx of mammographic masses and clustered microcalcifications: a review

M Elter, A Horsch - Medical physics, 2009 - Wiley Online Library
Breast cancer is the most common type of cancer among women in the western world. While
mammography is regarded as the most effective tool for the detection and diagnosis of …

A bottom-up review of image analysis methods for suspicious region detection in mammograms

P Oza, P Sharma, S Patel, A Bruno - Journal of Imaging, 2021 - mdpi.com
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 …

Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications

AN Karahaliou, IS Boniatis… - IEEE transactions on …, 2008 - ieeexplore.ieee.org
The current study investigates texture properties of the tissue surrounding microcalcification
(MC) clusters on mammograms for breast cancer diagnosis. The case sample analyzed …

Topological modeling and classification of mammographic microcalcification clusters

Z Chen, H Strange, A Oliver… - IEEE transactions on …, 2014 - ieeexplore.ieee.org
Goal: The presence of microcalcification clusters is a primary sign of breast cancer; however,
it is difficult and time consuming for radiologists to classify microcalcifications as malignant …

Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis

A Karahaliou, S Skiadopoulos, I Boniatis… - The British journal of …, 2007 - academic.oup.com
Diagnosis of microcalcifications (MCs) is challenged by the presence of dense breast
parenchyma, resulting in low specificity values and thus in unnecessary biopsies. The …

Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications

C Lei, W Wei, Z Liu, Q **ong, C Yang, M Yang… - European journal of …, 2019 - Elsevier
Purpose We developed and validated a radiomic model based on mammography and
assessed its value for predicting the pathological diagnosis of Breast Imaging Reporting and …