Harnessing Fusion Modeling for Enhanced Breast Cancer Classification through Interpretable Artificial Intelligence and In-Depth Explanations

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 136, P. 108939 - 108939

Published: July 17, 2024

Language: Английский

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Computationally efficient LC-SCS deep learning model for breast cancer classification using thermal imaging

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(26), P. 16233 - 16250

Published: May 23, 2024

Language: Английский

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Breast Cancer Molecular Subtype Prediction: A Mammography-Based AI Approach

Biomedicines, Journal Year: 2024, Volume and Issue: 12(6), P. 1371 - 1371

Published: June 20, 2024

Breast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the subtype requires biopsy—a specialized, expensive, time-consuming procedure, often yielding to results that must be supported additional biopsies due technique errors or tumor heterogeneity. This study introduces novel approach for predicting breast using mammography images advanced artificial intelligence (AI) methodologies. Using OPTIMAM imaging database, 1397 from 660 patients were selected. The pretrained deep learning model ResNet-101 was employed classify tumors into five subtypes: Luminal A, B1, B2, HER2, Triple Negative. Various classification strategies studied: binary classifications (one vs. all others, specific combinations) multi-class (evaluating simultaneously). To address imbalanced data, like oversampling, undersampling, data augmentation explored. Performance evaluated accuracy area under receiver operating characteristic curve (AUC). Binary showed maximum average AUC 79.02% 64.69%, respectively, while achieved an 60.62% oversampling augmentation. most notable HER2 non-HER2, 89.79% 73.31%. combinations revealed 76.42% A 73.04% B1. These findings highlight potential mammography-based AI non-invasive prediction, offering promising alternative paving way personalized plans.

Language: Английский

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HAFMAB-Net: hierarchical adaptive fusion based on multilevel attention-enhanced bottleneck neural network for breast histopathological cancer classification

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(5)

Published: March 19, 2025

Language: Английский

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An Intelligent Approach for Automating Robotic Arm Maneuvering in Endometriosis Surgery

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 2, 2025

Artificial intelligence (AI) and computer vision are revolutionizing numerous fields, including robotic surgery, which stands to benefit immensely from advances in machine learning methodologies. While prior research has extensively focused on disorder detection, localization, semantic segmentation, the crucial challenge of arm maneuvering during autonomous surgeries remains underexplored. This study proposes a robust interpretable approach enable robots autonomously execute endometriosis by skillfully navigating their arms, equipped with camera surgical tools such as graspers or lasers. A decision tree framework is developed assess robot's real-time status guide its actions at every stage. integrates diverse ensemble neural network models for classification segmentation support decision-making. Specifically, proposed utilize deep image quality, identify obstructions caused adhesions, detect anatomical targets (e.g., uterus peritoneum), determine proximity ovary uterus. The further enhance accuracy detecting localizing ovary. By employing these frameworks within model, this work aims advance surgery capabilities, enabling fully autonomous, reliable, efficient operations. Consequently, method minimize economic costs, bleeding, post-operative pain, infection risk, while optimizing precision performance.

Language: Английский

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A hybrid deep learning model for mammographic breast cancer detection: Multi-autoencoder and attention mechanisms

Journal of Radiation Research and Applied Sciences, Journal Year: 2025, Volume and Issue: 18(3), P. 101578 - 101578

Published: May 8, 2025

Language: Английский

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Breast Cancer Molecular Subtype Detection through Mammograms with Machine Learning: A Comprehensive Framework Using Radiomics and Metaheuristic Optimization

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 258, P. 3211 - 3220

Published: Jan. 1, 2025

Language: Английский

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A Novel Diagnostic Framework for Breast Cancer: Combining Deep Learning with Mammogram-DBT Feature Fusion

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103836 - 103836

Published: Dec. 1, 2024

Language: Английский

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Hybrid Feature Extraction for Breast Cancer Classification Using the Ensemble Residual VGG16 Deep Learning Model

Current Bioinformatics, Journal Year: 2024, Volume and Issue: 20(2), P. 149 - 163

Published: Oct. 30, 2024

Introduction: Breast Cancer (BC) is a significant cause of high mortality amongst women globally and probably will remain disease posing challenges about its detectability. Advancements in medical imaging technology have improved the accuracy efficiency breast cancer classification. However, tumor features' complexity data variability still pose challenges. Method: This study proposes Ensemble Residual-VGG-16 model as novel combination Deep Residual Network (DRN) VGG-16 architecture. purposely engineered with maximal precision for task diagnosis based on mammography images. We assessed performance by accuracy, recall, precision, F1-Score. All these metrics indicated this model. The diagnostic residual-VGG16 performed exceptionally well an 99.6%, 99.4%, recall 99.7%, F1 score 98.6%, Mean Intersection over Union (MIoU) 99.8% MIAS datasets. Result: Similarly, INBreast dataset achieved 93.8%, 94.2%, 94.5%, F1-score 93.4%. Conclusion: proposed advancement diagnosis, potential automated grading.

Language: Английский

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NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset

Computer Methods and Programs in Biomedicine, Journal Year: 2024, Volume and Issue: 254, P. 108291 - 108291

Published: June 18, 2024

Language: Английский

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