Pattern classification for breast lesion on FFDM by integration of radiomics and deep features

Zhang, Xinyu; Liang, Cuixia; Jiang, Xiaocong; Zhong, Rikui; Lan, Yuhong; Ma, Jianhua; Bai, Libing

doi:10.1016/j.compmedimag.2021.101922

Cited by 4 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Logistic Regression (Zhang et al, 2021a), Logistic Regression is a linear model used in binary classification to predict the likelihood of an instance belonging to a specific class. Boosting Algorithms -Grad Boost, XG Boost Extreme learning machine (ELM) (Ibrahim et al, 2023), (Sajid et al, 2023;Song et al, 2020) (Wang et al, 2019), (Sannasi Chakravarthy et al, 2022;Singh et al, 2023;Chakravarthy et al, 2023b;Hosni Mahmoud et al, 2022) Boosting algorithms are ensemble learning strategies that improve overall predictive performance by successively combining weak learners to create a strong learner.…”

Section: Continuation Ofmentioning

confidence: 99%

“…Inception-v3 improved the model by using factorised convolutions and dimension reductions. (Babu and Jerome, 2022;Mahmood et al, 2022;Shen et al, 2020;Wang et al, 2019;Zhang et al, 2021a), (Albalawi et al, 2022;Cai et al, 2021;Chandraraju and Jeyaprakash, 2022;Gargouri et al, 2022;George Melekoodappattu et al, 2022;Jones et al, 2023;Kurek et al, 2018;Mahmoud et al, 2021;Mokni and Haoues, 2022;Patil and Biradar, 2021;Patil et al, 2022;Razali et al, 2023a;Sajid et al, 2023;Shivhare and Saxena, 2022;Arputham et al, 2021;Chouhan et al, 2021;Chugh et al, 2022;Ramesh et al, 2022;Sivakrithika and Dinakaran, 2018;Ranjbarzadeh et al, 2023a;Song et al, 2020;Yu et al, 2023c;Elmoufidi, 2022;Hosni Mahmoud et al, 2022) (Babu andJerome, 2022), (Razali et al, 2023a;Arputham et al, 2021;Chouhan et al, 2021) (Mahmood et al, 2022;Shen et al, 2020;Wang et al, 2019;Zhang et al, 2021a), (Albalawi et al, 2022;Razali e...…”

Section: Rq2: What Are the Unique Challenges And Considerations In Ap...mentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective

Amin,

Acharya,

et al. 2024

Preprint

View full text Add to dashboard Cite

Breast cancer (BC) is a leading cause of mortality in women worldwide, and its incidence and prognosis vary greatly, especially in low-income areas such as Asia and Africa, compared to America and Europe. This review examines the critical role of computer-aided diagnostic systems utilizing Deep Learning (DL) techniques in improving the precision of BC detection, which can help researchers and practitioners better understand the obstacles and emerging trends in the field. The comprehensive analysis and synthesis of the published articles is provided by the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA). Following several searches, 287 articles were found eligible for the assessment followed by data extraction and synthesis. This work provides insight into the pathology of BC and its corresponding mammographic appearance, a comprehensive analysis of publication trends, important contributors, and emerging themes using bibliometric techniques in addition to a thorough review of research articles. Focus maps were also created to identify and shed light on the body of knowledge. In contrast to other research, this review sheds light on the subtle identification of breast density, mass, and calcification from mammography images by emphasizing the critical evaluation of image pre- processing, augmentation, detection, segmentation, and classification techniques used from 2018 to 2023, further highlighting articles related to the Asian dataset. Furthermore, the review examines contributions made by authors, countries, and experimental datasets, providing insights into various approaches. Prospero registration: CRD42022478896

show abstract