Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine

Journal of Personalized Medicine, Journal Year: 2024, Volume and Issue: 14(8), P. 792 - 792

Published: July 26, 2024

Early detection of breast cancer is essential for increasing survival rates, as it one the primary causes death women globally. Mammograms are extensively used by physicians diagnosis, but selecting appropriate algorithms image enhancement, segmentation, feature extraction, and classification remains a significant research challenge. This paper presents computer-aided diagnosis (CAD)-based hybrid model combining convolutional neural networks (CNN) with pruned ensembled extreme learning machine (HCPELM) to enhance detection, classification. The employs rectified linear unit (ReLU) activation function data analytics after removing artifacts pectoral muscles, HCPELM hybridized CNN improves extraction. elements fully connected layers. Convolutional layers extract spatial features like edges, textures, more complex in deeper take these combine them non-linear manner perform final ELM performs recognition tasks, aiming state-of-the-art performance. classifier transfer freezing certain modifying architecture reduce parameters, easing detection. was trained using MIAS database evaluated against benchmark methods. It achieved accuracy 86%, outperforming deep models. demonstrating superior performance early thus aiding healthcare practitioners diagnosis.

Language: Английский

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Multistage transfer learning for medical images

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(9)

Published: Aug. 6, 2024

Abstract Deep learning is revolutionizing various domains and significantly impacting medical image analysis. Despite notable progress, numerous challenges remain, necessitating the refinement of deep algorithms for optimal performance in This paper explores growing demand precise robust analysis by focusing on an advanced technique, multistage transfer learning. Over past decade, has emerged as a pivotal strategy, particularly overcoming associated with limited data model generalization. However, absence well-compiled literature capturing this development remains gap field. exhaustive investigation endeavors to address providing foundational understanding how approaches confront unique posed insufficient datasets. The offers detailed types, architectures, methodologies, strategies deployed Additionally, it delves into intrinsic within framework, comprehensive overview current state while outlining potential directions advancing methodologies future research. underscores transformative analysis, valuable guidance researchers healthcare professionals.

Language: Английский

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Enhancing Medical Image Quality Using Fractional Order Denoising Integrated with Transfer Learning

Fractal and Fractional, Journal Year: 2024, Volume and Issue: 8(9), P. 511 - 511

Published: Aug. 29, 2024

In medical imaging, noise can significantly obscure critical details, complicating diagnosis and treatment. Traditional denoising techniques often struggle to maintain a balance between reduction detail preservation. To address this challenge, we propose an “Efficient Transfer-Learning-Based Fractional Order Image Denoising Approach in Medical Analysis (ETLFOD)” method. Our approach uniquely integrates transfer learning with fractional order techniques, leveraging pre-trained models such as DenseNet121 adapt the specific needs of image denoising. This method enhances performance while preserving essential details. The ETLFOD model has demonstrated superior compared state-of-the-art (SOTA) techniques. For instance, our achieved accuracy 98.01%, precision 98%, recall outperforming traditional methods. Specific results include 95% accuracy, 98% precision, 99% recall, 96% F1-score for MRI brain datasets, 88% 91% COVID-19 lung data. X-ray pneumonia CT dataset showed 92% 97% 93% F1-score. It is important note that report metrics paper, primary evaluation based on comparison original noisy images denoised outputs, ensuring focus quality enhancement rather than classification performance.

Language: Английский

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Advancing Breast Cancer Diagnosis: Integrating Deep Transfer Learning and U-Net Segmentation for Precise Classification and Delineation of Ultrasound Images

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105047 - 105047

Published: April 1, 2025

Language: Английский

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Advancements and implications of artificial intelligence for early detection, diagnosis and tailored treatment of cancer

Seminars in Oncology, Journal Year: 2025, Volume and Issue: 52(3), P. 152349 - 152349

Published: May 8, 2025

Language: Английский

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Enhancing Breast Cancer Detection in Ultrasound Images: An Innovative Approach Using Progressive Fine‐Tuning of Vision Transformer Models

International Journal of Intelligent Systems, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Breast cancer is ranked as the second most common among women globally, highlighting critical need for precise and early detection methods. Our research introduces a novel approach classifying benign malignant breast ultrasound images. We leverage advanced deep learning methodologies, mainly focusing on vision transformer (ViT) model. method distinctively features progressive fine‐tuning, tailored process that incrementally adapts model to nuances of tissue classification. Ultrasound imaging was chosen its distinct benefits in medical diagnostics. This modality noninvasive cost‐effective demonstrates enhanced specificity, especially dense tissues where traditional methods may struggle. Such characteristics make it an ideal choice sensitive task detection. extensive experiments utilized images dataset, comprising 780 both tissues. The dataset underwent comprehensive analysis using several pretrained models, including VGG16, VGG19, DenseNet121, Inception, ResNet152V2, DenseNet169, DenseNet201, ViT. results presented were achieved without employing data augmentation techniques. ViT demonstrated robust accuracy generalization capabilities with original size, which consisted 637 Each model’s performance meticulously evaluated through 10‐fold cross‐validation technique, ensuring thorough unbiased comparison. findings are significant, demonstrating fine‐tuning substantially enhances capability. resulted remarkable 94.49% AUC score 0.921, significantly higher than models fine‐tuning. These affirm efficacy highlight transformative potential integrating image classification tasks. study solidifies role such methodologies improving diagnosis, when coupled unique advantages imaging.

Language: Английский

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Machine learning and pathology: a historical perspective

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 35 - 75

Published: Nov. 29, 2024

Language: Английский

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Integrating Machine Learning to Customize Chemotherapy for Oral Cancer Patients

Oral Oncology Reports, Journal Year: 2024, Volume and Issue: unknown, P. 100711 - 100711

Published: Dec. 1, 2024

Language: Английский

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