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

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

Khalil Ur Rehman

Jianqiang Li

Yan Pei

et al.

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Cited by 3 publications

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Intelligent breast cancer diagnosis with two-stage using mammogram images

Yaqub,

Jinchao,

Aijaz

et al. 2024

Sci Rep

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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.

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Intelligent breast cancer diagnosis with two-stage using mammogram images

Yaqub,

Jinchao,

Aijaz

et al. 2024

Sci Rep

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Pei

2022

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Fraud Detection in NoSQL Database Systems using Advanced Machine Learning

Arjunan

2024

International Journal of Innovative Science and Research Techno

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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.

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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

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