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Breast Cancer Detection and Classification Using Hybrid Feature Selection and DenseXtNet Approach

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1 of the 33 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-11): 10.1155/2022/1359019 (notice 2023-06-28).

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cncr.32859openalex-snapshot

Mammography screening reduces rates of advanced and fatal breast cancers: Results in 549,091 women

inferredcites →https://doi.org/10.1002/ima.22400openalex-snapshot

Deep learning and optimization algorithms for automatic breast cancer detection

inferredcites →https://doi.org/10.1002/ima.22488openalex-snapshot

Thermography based breast cancer detection using self‐adaptive gray level histogram equalization color enhancement method

inferredcites →https://doi.org/10.1007/978-3-030-33128-3openalex-snapshot

Deep Learning in Medical Image Analysis

inferredcites →https://doi.org/10.1007/bf01617722openalex-snapshot

Classification-algorithm evaluation: Five performance measures based onconfusion matrices

inferredcites →https://doi.org/10.1007/s10278-020-00330-4openalex-snapshot

Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning

inferredcites →https://doi.org/10.1007/s11042-020-09991-3openalex-snapshot

Automatic computer-aided diagnosis system for mass detection and classification in mammography

inferredcites →https://doi.org/10.1007/s11760-021-01882-wopenalex-snapshot

Automated early breast cancer detection and classification system

inferredcites →https://doi.org/10.1007/s11912-021-01069-zopenalex-snapshot

Global Perceptions of Women on Breast Cancer and Barriers to Screening

inferredcites →https://doi.org/10.1007/s12065-020-00403-xopenalex-snapshot

Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network

inferredcites →https://doi.org/10.1016/j.acra.2018.01.023openalex-snapshot

Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features

inferredcites →https://doi.org/10.1016/j.acra.2018.06.020openalex-snapshot

Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset

inferredcites →https://doi.org/10.1016/j.cmpb.2020.105584openalex-snapshot

Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms

inferredcites →https://doi.org/10.1016/j.compbiomed.2021.104245openalex-snapshot

A framework for breast cancer classification using Multi-DCNNs

inferredcites →https://doi.org/10.1016/j.imu.2019.100219openalex-snapshot

Prediction of Pathological Complete Response after Neoadjuvant Chemotherapy for breast cancer using ensemble machine learning

inferredcites →https://doi.org/10.1016/j.ipm.2009.03.002openalex-snapshot

A systematic analysis of performance measures for classification tasks

inferredcites →https://doi.org/10.1016/j.measurement.2019.05.083openalex-snapshot

Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform

inferredcites →https://doi.org/10.1016/j.patrec.2019.03.022openalex-snapshot

A novel deep learning based framework for the detection and classification of breast cancer using transfer learning

inferredcites →https://doi.org/10.1038/ki.2009.92openalex-snapshot

Diagnostic methods I: sensitivity, specificity, and other measures of accuracy

inferredcites →https://doi.org/10.1038/s41598-020-69800-7openalex-snapshot

Spectrochemical analysis of liquid biopsy harnessed to multivariate analysis towards breast cancer screening

inferredcites →https://doi.org/10.1109/access.2021.3079204openalex-snapshot

A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique

inferredcites →https://doi.org/10.1155/2022/1359019openalex-snapshot

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

inferredcites →https://doi.org/10.1177/1063293x221080518openalex-snapshot

Computer-aided diagnosis for breast cancer detection and classification using optimal region growing segmentation with MobileNet model

inferredcites →https://doi.org/10.1177/201010581102000411openalex-snapshot

Measures of Diagnostic Accuracy: Sensitivity, Specificity, PPV and NPV

inferredcites →https://doi.org/10.1186/s12864-019-6413-7openalex-snapshot

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

inferredcites →https://doi.org/10.14569/ijacsa.2022.0130729openalex-snapshot

Breast Cancer Detection and Classification using Deep Learning Xception Algorithm

inferredcites →https://doi.org/10.3390/app12073273openalex-snapshot

TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages

inferredcites →https://doi.org/10.3390/app12094616openalex-snapshot

Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images

inferredcites →https://doi.org/10.3390/biology10121347openalex-snapshot

BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images

inferredcites →https://doi.org/10.3390/biology11030439openalex-snapshot

Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images

inferredcites →https://doi.org/10.3390/diagnostics12081812openalex-snapshot

Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques

inferredcites →https://doi.org/10.3390/jpm10040211openalex-snapshot

Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning

inferredcites →https://doi.org/10.9734/jamcs/2019/v32i130137openalex-snapshot

Design and Implementation of a Fuzzy Expert System for Diagnosing Breast Cancer

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-63772-8_15openalex-snapshot

Enhancing Breast Cancer Diagnosis: A CNN-Based Approach for Medical Image Segmentation and Classification

inferred← citeshttps://doi.org/10.1007/978-3-032-25172-5_1openalex-snapshot

Artificial Intelligence and Machine Learning in the Attribution of Art Works

inferred← citeshttps://doi.org/10.1007/978-3-032-27563-9_6openalex-snapshot

Deep Learning Model with Proposed Correlation-Based Deepjoint Segmentation Technique for Breast Cancer Detection Using Mammogram Image

inferred← citeshttps://doi.org/10.1007/s41742-026-01151-zopenalex-snapshot

A Novel Deep Learning Approach for Multispectral Land Use/Land Cover Classification Using Optimized Feature Selection and Multi-scale Segmentation

inferred← citeshttps://doi.org/10.1016/j.asoc.2025.113713openalex-snapshot

A feature selection framework using an elitist quasi-opposition self-adaptive partial reinforcement optimization algorithm for breast cancer detection in mammograms

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.111467openalex-snapshot

Explainable Hybrid Swin ConvFusionTransformer based deep learning with feature extractor for breast cancer detection

inferred← citeshttps://doi.org/10.1016/j.trac.2025.118412openalex-snapshot

Analytical strategies in early breast cancer diagnostic biomarker discovery by machine learning methods: Promises, advances and outlooks

inferred← citeshttps://doi.org/10.1038/s41598-025-09554-2openalex-snapshot

New hybrid features extracted from US images for breast cancer classification

inferred← citeshttps://doi.org/10.1038/s41598-025-31339-wopenalex-snapshot

Comparative analysis of novel preprocessing techniques and deep learning based multi modal feature fusion for diabetic retinopathy grading

inferred← citeshttps://doi.org/10.1109/gecost66002.2025.11324827openalex-snapshot

Hybrid GANs and Metaheuristic-Based Deep Learning: Transforming Breast Cancer Data Interpretation Using Multiple Models

inferred← citeshttps://doi.org/10.1109/icsipa62061.2024.10686088openalex-snapshot

On Handcrafted Machine Learning Features for Art Authentication

inferred← citeshttps://doi.org/10.1109/icsipa62061.2024.10686516openalex-snapshot

A One-Class Autoencoder for Porcelain Art Authentication

inferred← citeshttps://doi.org/10.1111/arcm.70140openalex-snapshot

One‐Class Autoencoders for Porcelain Art Attribution: The Case of William Billingsley

inferred← citeshttps://doi.org/10.1155/int/1034463openalex-snapshot

SpinalDenseXtNet: An Explainable Hybrid Deep Learning Framework for Mammographic Breast Cancer Classification

inferred← citeshttps://doi.org/10.23919/indiacom70271.2026.11526642openalex-snapshot

Efficient Feature Selection of Deep Features for Breast Cancer Detection Using Nature-Inspired Optimization

inferred← citeshttps://doi.org/10.3390/electronics14040692openalex-snapshot

Synergy of Internet of Things and Software Engineering Approach for Enhanced Copy–Move Image Forgery Detection Model

inferred← citeshttps://doi.org/10.35377/saucis...1711862openalex-snapshot

Precision in Breast Cancer Detection with Advanced Deep Learning for Lesion Localization and Classification

inferred← citeshttps://doi.org/10.4018/979-8-3693-9816-6.ch008openalex-snapshot

Convolutional Neural Networks (CNNs) for Enhanced Decision-Making in Medical Imaging

inferred← citeshttps://doi.org/10.54254/2753-8818/33/20240914openalex-snapshot

Study on breast cancer image detection and classification based on residual connected convolutional neural network (CNN)

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