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Deep‐features with Bayesian optimized classifiers for the breast cancer diagnosis
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Detection and classification of microcalcification from digital mammograms with firefly algorithm, extreme learning machine and non‐linear regression models: A comparison
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Recent advances in breast cancer research impacting clinical diagnostic practice
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Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks
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Deep Learning of Representations: Looking Forward
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Mammography: How to Interpret Microcalcifications
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Using computer-aided detection in mammography as a decision support
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Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
inferredcites →https://doi.org/10.1007/s10044-018-0760-xopenalex-snapshot
Feature subset selection for classification of malignant and benign breast masses in digital mammography
inferredcites →https://doi.org/10.1007/s11222-016-9696-4openalex-snapshot
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
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Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension
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Automated detection of breast tumors using the asymmetry approach
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A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm
inferredcites →https://doi.org/10.1016/j.artmed.2006.03.002openalex-snapshot
Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers
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Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier
inferredcites →https://doi.org/10.1016/j.asoc.2016.01.039openalex-snapshot
Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm
inferredcites →https://doi.org/10.1016/j.asoc.2016.04.036openalex-snapshot
Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images
inferredcites →https://doi.org/10.1016/j.bspc.2010.10.003openalex-snapshot
Directional features for automatic tumor classification of mammogram images
inferredcites →https://doi.org/10.1016/j.bspc.2020.101912openalex-snapshot
Automated breast cancer detection in digital mammograms: A moth flame optimization based ELM approach
inferredcites →https://doi.org/10.1016/j.catena.2016.06.004openalex-snapshot
Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size
inferredcites →https://doi.org/10.1016/j.cmpb.2015.09.019openalex-snapshot
An automated confirmatory system for analysis of mammograms
inferredcites →https://doi.org/10.1016/j.cmpb.2015.10.017openalex-snapshot
Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: A comparative analysis
inferredcites →https://doi.org/10.1016/j.cmpb.2015.12.014openalex-snapshot
Representation learning for mammography mass lesion classification with convolutional neural networks
inferredcites →https://doi.org/10.1016/j.compbiomed.2009.05.002openalex-snapshot
Development of tolerant features for characterization of masses in mammograms
inferredcites →https://doi.org/10.1016/j.compbiomed.2013.03.008openalex-snapshot
Mammographical mass detection and classification using Local Seed Region Growing–Spherical Wavelet Transform (LSRG–SWT) hybrid scheme
inferredcites →https://doi.org/10.1016/j.compmedimag.2016.07.004openalex-snapshot
Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data
inferredcites →https://doi.org/10.1016/j.eswa.2015.07.072openalex-snapshot
Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern
inferredcites →https://doi.org/10.1016/j.eswa.2017.05.062openalex-snapshot
Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex wavelet transform
inferredcites →https://doi.org/10.1016/j.eswa.2018.08.021openalex-snapshot
A generalized mean distance-based k-nearest neighbor classifier
inferredcites →https://doi.org/10.1016/j.irbm.2020.12.004openalex-snapshot
Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning
inferredcites →https://doi.org/10.1016/j.neucom.2014.12.032openalex-snapshot
Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer
inferredcites →https://doi.org/10.1016/j.neuroimage.2014.12.061openalex-snapshot
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
inferredcites →https://doi.org/10.1016/j.procs.2015.07.341openalex-snapshot
Mammogram Classification using Law's Texture Energy Measure and Neural Networks
inferredcites →https://doi.org/10.1016/j.zemedi.2018.12.003openalex-snapshot
A gentle introduction to deep learning in medical image processing
inferredcites →https://doi.org/10.1088/1748-0221/12/07/p07009openalex-snapshot
Application of texture analysis method for mammogram density classification
inferredcites →https://doi.org/10.1109/42.650876openalex-snapshot
Measures of acutance and shape for classification of breast tumors
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvprw.2017.122openalex-snapshot
Evaluating State-of-the-Art Object Detector on Challenging Traffic Light Data
inferredcites →https://doi.org/10.1109/icccnt.2018.8494076openalex-snapshot
Mammograms Classification Using Multiresolution Transforms and Convolution Neural Networks
inferredcites →https://doi.org/10.1109/mci.2018.2840738openalex-snapshot
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
inferredcites →https://doi.org/10.1109/msp.2019.2900993openalex-snapshot
From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities
inferredcites →https://doi.org/10.1109/tvlsi.2017.2784783openalex-snapshot
A Global Bayesian Optimization Algorithm and Its Application to Integrated System Design
inferredcites →https://doi.org/10.1109/ubmk.2017.8093429openalex-snapshot
Using deep learning for mammography classification
inferredcites →https://doi.org/10.1117/12.910710openalex-snapshot
Breast image feature learning with adaptive deconvolutional networks
inferredcites →https://doi.org/10.1177/0037549716667834openalex-snapshot
Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization
inferredcites →https://doi.org/10.30699/acadpub.mci.3.4.13openalex-snapshot
Detection and Classification of Breast Cancer in Mammography Images Using Pattern Recognition Methods
inferredcites →https://doi.org/10.3233/fi-2017-1487openalex-snapshot
Abnormal Breast Detection in Mammogram Images by Feed-forward Neural Network Trained by Jaya Algorithm
inferredcites →https://doi.org/10.3322/caac.21492openalex-snapshot
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
inferredcites →https://doi.org/10.3390/cancers7020815openalex-snapshot
Effects of Age on the Detection and Management of Breast Cancer
inferredcites →https://doi.org/10.3390/s18103376openalex-snapshot
Deep Learning Scene Recognition Method Based on Localization Enhancement
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1603.06560openalex-snapshot
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
inferredcites →https://doi.org/10.48550/arxiv.1705.06362openalex-snapshot
Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors
inferredcites →https://doi.org/10.9790/2834-1104013444openalex-snapshot
Improved Face Recognition Rate Using HOG Features and SVM Classifier
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpe.7231openalex-snapshot
A systematic review on machine learning algorithms used for forecasting lake‐water level fluctuations
inferred← citeshttps://doi.org/10.1002/cpe.7233openalex-snapshot
Breast cancer: Three‐class masses classification in mammograms using Apriori dynamic selection
inferred← citeshttps://doi.org/10.1002/ima.22781openalex-snapshot
SKMAT‐U‐Net architecture for breast mass segmentation
inferred← citeshttps://doi.org/10.1002/ima.23011openalex-snapshot
BCUIS‐Net : A breast cancer ultrasound image segmentation network via boundary‐aware and shape feature fusion
inferred← citeshttps://doi.org/10.1007/978-3-030-90724-2_5openalex-snapshot
Fusion of Deep Features for Classification of Breast Cancer Using Multi-Deep CNNs
inferred← citeshttps://doi.org/10.1007/s44174-025-00527-7openalex-snapshot
Optimized Lightweight Temporal Attention-Based Convolutional Neural Network Espoused Automatic Diagnosis and Classification of Breast Cancer
inferred← citeshttps://doi.org/10.1007/s44196-024-00593-7openalex-snapshot
Multi-class Breast Cancer Classification Using CNN Features Hybridization
inferred← citeshttps://doi.org/10.1016/j.irbm.2022.100749openalex-snapshot
Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer
inferred← citeshttps://doi.org/10.1063/5.0125243openalex-snapshot
Deep learning-based breast cancer detection using VGG-NiN architecture
inferred← citeshttps://doi.org/10.1063/5.0184603openalex-snapshot
Performance evaluation of machine learning techniques for breast cancer detection using WDBC dataset
inferred← citeshttps://doi.org/10.1109/stcr51658.2021.9588828openalex-snapshot
A Systematic Review on Screening, Examining and Classification of Breast Cancer
inferred← citeshttps://doi.org/10.1145/3571560.3571572openalex-snapshot
A Comprehensive Study on Machine Learning in Breast Cancer Detection and Classification
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4181414/v1openalex-snapshot
A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective
inferred← citeshttps://doi.org/10.3390/su151612119openalex-snapshot
Meteorological-Data-Driven Rubber Tree Powdery Mildew Model and Its Application on Spatiotemporal Patterns: A Case Study of Hainan Island
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