Detection of epileptic seizure using EEG signals analysis based on deep learning techniques

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 181, P. 114700 - 114700

Published: March 11, 2024

Language: Английский

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An integrated hybrid deep learning data driven approaches for spatiotemporal mapping of land susceptibility to salt/dust emissions

Advances in Space Research, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

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Adam golden search optimization enabled DCNN for classification of breast cancer using histopathological image

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 94, P. 106239 - 106239

Published: April 4, 2024

Language: Английский

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Fluorescence microscopy and histopathology image based cancer classification using graph convolutional network with channel splitting

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 103, P. 107400 - 107400

Published: Jan. 6, 2025

Language: Английский

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EMViT‐BCC: Enhanced Mobile Vision Transformer for Breast Cancer Classification

International Journal of Imaging Systems and Technology, Journal Year: 2025, Volume and Issue: 35(2)

Published: March 1, 2025

ABSTRACT Breast cancer (BC) accounts for most cancer‐related deaths worldwide, so it is crucial to consider as a prominent issue and emphasize proper diagnosis timely detection. This study introduces deep learning strategy called EMViT‐BCC the BC histopathology image classification two class eight class. The proposed model utilizes Mobile Vision Transformer (MobileViT) block, which captures local global features extracts necessary task. approach trained evaluated on standard BreaKHis dataset. with both original raw images well stain‐normalized analysis of Extensive experiments demonstrate that achieves higher accuracy robustness in classifying benign malignant identifying various subtypes BC. Our results by incorporating further layers, performance MobileViT can be greatly enhanced, 99.43% two‐class 93.61% eight‐class classification. These findings suggest while stain normalization standardize variations, data retain details enhance performance. In comparison existing works, methodology surpasses state‐of‐the‐art (SOTA) methods offers promising solution reliable binary multi‐class.

Language: Английский

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Image‐Based Breast Cancer Histopathology Classification and Diagnosis Using Deep Learning Approaches

Applied Computational Intelligence and Soft Computing, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Breast cancer is characterized by abnormal cell growth, which leads to tumor formation. Autonomous breast detection has seen good progress. However, there are still several challenges robust detection. This survey article explores the complexities inherent in multiclass classification for diagnosis, aiming improve patient care, efficiency, and timeliness. In our study, we focus on using histopathology slide images assess current state of classification, particularly with artificial intelligence, specifically deep learning convolutional neural networks. The key tools diagnosis process, allowing pathologists visually tissue samples signs cancer. Our analysis reveals that hinder effectiveness diagnostic methods. One significant issue need more diversity existing datasets, often fail represent a wide range populations. limitation reduces accuracy results, mainly when applied different clinical environments. Furthermore, class imbalances within these where certain types or stages underrepresented, lead biased diagnoses, common cases being easily identified while rarer frequently missed. Another challenge limited generalizability techniques, perform well controlled environments but new, unseen data from institutions imaging systems. Additionally, complexity histopathological means it can be difficult clinicians interpret findings, leading uncertainty process. study addressing issues requires collaborative efforts quality reduce imbalances, develop optimal standardized By overcoming challenges, enhance accuracy, accessibility ultimately better outcomes global healthcare. We believe examining factors variables conducting an in‐depth art, this will contribute art benefit researchers both computing medical domains.

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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AI-Assisted Breast Cancer Prediction, Classification, and Future Directions: A Narrative Review Involving Histopathological Image Datasets

The Open Public Health Journal, Journal Year: 2025, Volume and Issue: 18(1)

Published: Feb. 7, 2025

Breast cancer-related deaths in women have increased significantly the past decade, emphasizing need for an accurate and early diagnosis. AI-assisted diagnosis using deep learning machine (DML) approaches has become a key method analysing breast tissue identifying tumour stages. DML algorithms are particularly effective classifying cancer images due to their ability handle large datasets, work with unstructured data, generate automated features, improve over time. However, performance of these models is heavily on datasets used training, performing inconsistently between different datasets. Given prediction that by 2050, there will be more than 30 million new cases 10 worldwide, it crucial focus recent advancements histopathological image systems. Histopathological provide critical information identify abnormalities, which directly impact model performance. This review discusses analyses various DML-based implementation, highlighting research gaps offering suggestions future improvements. The goal develop efficient early-stage cancer. In addition, this detection assists healthcare professional guiding prevention methods smart

Language: Английский

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Smart neural network and cognitive computing process for multi task nuclei detection segmentation and classification in breast cancer histopathology images

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 26, 2025

Abstract The detection, segmentation, and differentiation of benign malignant nuclei from the histopathology images is a challenging task for early diagnosis breast cancer. Misinterpretation True Negative (TN) False Positive (FP) can generate incorrect results. proposed Cognitive Computing Process (CCP) detects segments using Deep U-Net with Spatial Attention Mechanisms (SAM) microns-per-pixel measurements to accurately locate assess density. To separate malignant, patches are introduced leverage model’s learning process. Smart Neural Network (SNN) models contain Convolutional (SCNN) (DCNN) reduce Proposed CCP SNN were evaluated BreakHis dataset, which contains 5547 samples at various magnifications (40×, 100×, 200×, 400×). These processed into patches, totaling 11,642, 9282, 9102, 9678 each 224 × pixels. model outperformed state-of-the-art UNet, Residual UNet (ResUNet), Long Short-Term Memory (CNN-LSTM) Dice coefficient 99.90%, an F1-score 99.04%, precision 99.80%, recall 99.76%. process began rate 0.01 decay 0.8, SCNN achieved false negative positive rates 0.04 0.05 low-density 400× 40× magnification, respectively. In contrast, recorded 0.02 0.01. For high-density FN FP 0.0 0.08, while DCNN reported 0.09 0.0. Networks high (77–99%), (75–99%), AUC 86–100%. combination improved accuracy over existing CNN like ResNet50, VGG19, DenseNet109, DenseNet201, VGG16. An ablation study showed p-value 0.00003 based on AUC, highlighting potential enhance automated cancer support clinical decision-making.

Language: Английский

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InceptionNeXt-Transformer: A novel multi-scale deep feature learning architecture for multimodal breast cancer diagnosis

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 110, P. 108116 - 108116

Published: May 29, 2025

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