Self-attention random forest for breast cancer image classification

Frontiers in Oncology, Journal Year: 2023, Volume and Issue: 13

Published: Feb. 6, 2023

Introduction Early screening and diagnosis of breast cancer can not only detect hidden diseases in time, but also effectively improve the survival rate patients. Therefore, accurate classification images becomes key to auxiliary diagnosis. Methods In this paper, on basis extracting multi-scale fusion features using pyramid gray level co-occurrence matrix, we present a Self-Attention Random Forest (SARF) model as classifier explain importance features, perform adaptive refinement processing thus, accuracy be improved. addition, use GridSearchCV technique optimize hyperparameters model, which greatly avoids limitation artificially selected parameters. Results To demonstrate effectiveness our method, validation histopathological image-BreaKHis. The proposed method achieves an average 92.96% micro AUC value 0.9588 for eight-class classification, 97.16% 0.9713 binary BreaKHis dataset. Discussion For sake verify universality conduct experiments MIAS An excellent is 98.79% Compared other state-of-the-art methods, experimental results that performance superior others. Furthermore, analyze influence different types provide theoretical further optimization future.

Language: Английский

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Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

Diagnostics, Journal Year: 2024, Volume and Issue: 14(4), P. 422 - 422

Published: Feb. 14, 2024

Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, advent deep learning techniques has showcased notable potential elevating precision efficiency data analysis. The proposed work introduces novel approach that harnesses power Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it nuanced landscape breast histopathology. Our model, Learning-based concatenated exhibits substantial performance enhancements compared traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, DenseNet121—each Convolutional Neural Network architecture designed for classification tasks—this study meticulously tunes hyperparameters optimize model performance. implementation is systematically benchmarked against individual classifiers data. Remarkably, our achieves an impressive training accuracy 98%. outcomes experiments underscore efficacy this four-level advancing By synergizing strengths transfer learning, holds augment diagnostic capabilities pathologists, thereby contributing more informed personalized planning individuals diagnosed with cancer. This research heralds promising stride toward leveraging cutting-edge technology refine understanding management cancer, marking advancement intersection artificial intelligence healthcare.

Language: Английский

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Fine tuning deep learning models for breast tumor classification

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 10, 2024

Abstract This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training testing sets), followed by augmentation techniques. Both feature extraction classification tasks are employed a Custom CNN. experimental results show that proposed CNN model exhibits better performance with accuracy of 84% than applying same other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, ResNet50V2, present relatively lower accuracies, ranging 74 82%; these four models used as both extractors classifiers. To increase metrics, Grey Wolf Optimization (GWO), Modified Gorilla Troops (MGTO) metaheuristic optimizers applied each separately for hyperparameter tuning. In this case, model, refined MGTO optimization, reaches exceptional 93.13% in just 10 iterations, outperforming state-of-the-art methods, based on

Language: Английский

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Denoising and segmentation in medical image analysis: A comprehensive review on machine learning and deep learning approaches

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: May 17, 2024

Language: Английский

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Enhancing breast cancer diagnosis accuracy through genetic algorithm-optimized multilayer perceptron

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 7(4), P. 4433 - 4449

Published: June 6, 2024

Language: Английский

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CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis

Machine Learning Science and Technology, Journal Year: 2023, Volume and Issue: 4(2), P. 025021 - 025021

Published: May 4, 2023

Abstract Automatic identification of salient features in large medical datasets, particularly chest x-ray (CXR) images, is a crucial research area. Accurately detecting critical findings such as emphysema, pneumothorax, and chronic bronchitis can aid radiologists prioritizing time-sensitive cases screening for abnormalities. However, traditional deep neural network approaches often require bounding box annotations, which be time-consuming challenging to obtain. This study proposes an explainable ensemble learning approach, CX-Net, lung segmentation diagnosing disorders using CXR images. We compare four state-of-the-art convolutional models, including feature pyramid network, U-Net, LinkNet, customized U-Net model with ImageNet extraction, data augmentation, dropout regularizations. All models are trained on the Montgomery VinDR-CXR datasets without segmented ground-truth masks. To achieve explainability, we integrate SHapley Additive exPlanations (SHAP) gradient-weighted class activation mapping (Grad-CAM) techniques, enable better understanding decision-making process provide visual explanations regions within By employing ensembling, our outlier-resistant CX-Net achieves superior performance segmentation, Jaccard overlap similarity 0.992, Dice coefficients 0.994, precision 0.993, recall 0.980, accuracy 0.976. The proposed approach demonstrates strong generalization capabilities VinDr-CXR dataset first use these semantic semi-supervised localization. In conclusion, this paper presents Extensive experimental results show that method efficiently accurately extracts interest images from publicly available indicating its potential integration into clinical decision support systems. Furthermore, incorporating SHAP Grad-CAM techniques further enhances interpretability trustworthiness AI-driven diagnostic system.

Language: Английский

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Breast cancer detection using enhanced preprocessing techniques to trace accurate skin air interface

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3159, P. 020027 - 020027

Published: Jan. 1, 2025

Views Icon Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Twitter Facebook Reddit LinkedIn Tools Reprints and Permissions Cite Search Site Citation V. Vijaya Chamundeeswari, Gowri; Breast cancer detection using enhanced preprocessing techniques to trace accurate skin air interface. AIP Conf. Proc. 9 January 2025; 3159 (1): 020027. https://doi.org/10.1063/5.0247043 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Dropdown Menu input auto suggest filter your All ContentAIP Publishing PortfolioAIP Conference Proceedings Advanced |Citation

Language: Английский

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Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 11, 2025

Language: Английский

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Extracting Knowledge from Machine Learning Models to Diagnose Breast Cancer

Life, Journal Year: 2025, Volume and Issue: 15(2), P. 211 - 211

Published: Jan. 31, 2025

This study explored the application of explainable machine learning models to enhance breast cancer diagnosis using serum biomarkers, contrary many studies that focus on medical images and demographic data. The primary objective was develop are not only accurate but also provide insights into factors driving predictions, addressing need for trustworthy AI in healthcare. Several classification were evaluated, including OneR, JRIP, FURIA, J48, ADTree, Random Forest, all which known their explainability. dataset included a variety such as electrolytes, metal ions, marker proteins, enzymes, lipid profiles, peptide hormones, steroid hormone receptors. Forest model achieved highest accuracy at 99.401%, followed closely by ADTree 98.802%. OneR J48 98.204% accuracy. Notably, identified oxytocin key predictive biomarker, with most featuring it rules. Other significant parameters GnRH, β-endorphin, vasopressin, IRAP, APB, well like iron, cholinesterase, total protein, progesterone, 5-nucleotidase, BMI, considered clinically relevant pathogenesis. discusses roles development, thus underscoring potential enhancing early focusing explainability use biomarkers.The combination both can lead improved detection personalized treatments, emphasizing these methods clinical settings. markers additional research therapeutic targets pathogenesis deep understanding interactions, advancing approaches management.

Language: Английский

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A review paper on intelligent heuristic-based deep learning structure for the observation of breast cancer

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3262, P. 020010 - 020010

Published: Jan. 1, 2025

Language: Английский

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