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A review of feature selection methods with applications
Feature selection (FS) methods can be used in data pre-processing to achieve efficient data
reduction. This is useful for finding accurate data models. Since exhaustive search for …
reduction. This is useful for finding accurate data models. Since exhaustive search for …
Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform
Breast cancer is one of the significant health problems in the world. If these abnormalities in
breast cancer are detected early there is a maximum chance for recovery. For this early …
breast cancer are detected early there is a maximum chance for recovery. For this early …
Reviewing machine learning and image processing based decision-making systems for breast cancer imaging
Breast cancer (BC) is the leading cause of death among women worldwide. It affects in
general women older than 40 years old. Medical images analysis is one of the most …
general women older than 40 years old. Medical images analysis is one of the most …
An in-depth and contrasting survey of meta-heuristic approaches with classical feature selection techniques specific to cervical cancer
Data mining and machine learning algorithms' performance is degraded by data of high-
dimensional nature due to an issue called “curse of dimensionality”. Feature selection is a …
dimensional nature due to an issue called “curse of dimensionality”. Feature selection is a …
Trainable model based on new uniform LBP feature to identify the risk of the breast cancer
In develo** countries breast cancer has been found to be one of the diseases that
threatens the lives of women, and that is why finding ways of detecting efficiently is of great …
threatens the lives of women, and that is why finding ways of detecting efficiently is of great …
PCA-PNN and PCA-SVM based CAD systems for breast density classification
Kriti, J Virmani, N Dey, V Kumar - … of intelligent optimization in biology and …, 2015 - Springer
Early prediction of breast density is clinically significant as there is an association between
the risk of breast cancer development and breast density. In the present work, the …
the risk of breast cancer development and breast density. In the present work, the …
Hybrid fast unsupervised feature selection for high-dimensional data
The emergence of``curse of dimensionality” issue as a result of high reduces datasets
deteriorates the capability of learning algorithms, and also requires high memory and …
deteriorates the capability of learning algorithms, and also requires high memory and …
A multi-task fusion model based on a residual–Multi-layer perceptron network for mammographic breast cancer screening
Y Zhong, Y Piao, B Tan, J Liu - Computer Methods and Programs in …, 2024 - Elsevier
Background and objective Deep learning approaches are being increasingly applied for
medical computer-aided diagnosis (CAD). However, these methods generally target only …
medical computer-aided diagnosis (CAD). However, these methods generally target only …
Model-based optical and acoustical compensation for photoacoustic tomography of heterogeneous mediums
Photoacoustic tomography (PAT) is a non-invasive, high-resolution imaging modality,
capable of providing functional and molecular information of various pathologies, such as …
capable of providing functional and molecular information of various pathologies, such as …
Breast density classification using local quinary patterns with various neighbourhood topologies
This paper presents an extension of work from our previous study by investigating the use of
Local Quinary Patterns (LQP) for breast density classification in mammograms on various …
Local Quinary Patterns (LQP) for breast density classification in mammograms on various …