A review on machine learning techniques for the assessment of image grading in breast mammogram

Rehman, Khalil Ur; Li, Jianqiang; Pei, Yan; Yasin, Anaa

doi:10.1007/s13042-022-01546-2

Int. J. Mach. Learn. & Cyber.

2022

DOI: 10.1007/s13042-022-01546-2

|View full text |Cite

A review on machine learning techniques for the assessment of image grading in breast mammogram

Khalil Ur Rehman

Jianqiang Li

Yan Pei

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 101 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Intelligent breast cancer diagnosis with two-stage using mammogram images

Yaqub,

Jinchao,

Aijaz

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.

show abstract

Intelligent breast cancer diagnosis with two-stage using mammogram images

Yaqub,

Jinchao,

Aijaz

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

Meet the Editorial Board Member

Pei

2022

CPB

View full text Add to dashboard Cite

Fraud Detection in NoSQL Database Systems using Advanced Machine Learning

Arjunan

2024

International Journal of Innovative Science and Research Techno

View full text Add to dashboard Cite

NoSQL databases such as MongoDB and Cassandra have been rapidly adopted in recent years because of their high performance, flexibility, and scalability. These databases present new security issues compared to SQL databases. NoSQL databases are vulnerable to fraud, intrusions and data breaches due to their dynamic schemas, lack of control over access and the focus on availability. This paper examines how advanced machine-learning techniques can be used to enhance fraud and intrusion detection in NoSQL databases. We examine different machine-learning algorithms, including neural networks and support vector machines. Random forests, clustering, and random forests can be used to analyze large databases activity logs in order to identify anomalous patterns of access indicative of malicious behavior. We examine how these models are trained online to detect emerging threats, and we validate the techniques using proof-of concept experiments on a prototype NoSQL based database. Our results show high accuracy for detecting injection attacks, unauthorized query, and abnormal database traffic, with low false-positive rates.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A review on machine learning techniques for the assessment of image grading in breast mammogram

Cited by 3 publications

References 101 publications

Intelligent breast cancer diagnosis with two-stage using mammogram images

Intelligent breast cancer diagnosis with two-stage using mammogram images

Meet the Editorial Board Member

Fraud Detection in NoSQL Database Systems using Advanced Machine Learning

Contact Info

Product

Resources

About