An intelligent healthcare framework for breast cancer diagnosis based on the information fusion of novel deep learning architectures and improved optimization algorithm

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 137, P. 109152 - 109152

Published: Aug. 22, 2024

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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Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images

Journal of Applied Biomedicine, Journal Year: 2024, Volume and Issue: 44(3), P. 731 - 758

Published: July 1, 2024

Language: Английский

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Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Nov. 23, 2023

Abstract Non-Small cell lung cancer (NSCLC) is one of the most dangerous cancers, with 85% all new diagnoses and a 30–55% recurrence rate after surgery. Thus, an accurate prediction risk in NSCLC patients during diagnosis could be essential to drive targeted therapies preventing either overtreatment or undertreatment patients. The radiomic analysis CT images has already shown great potential solving this task; specifically, Convolutional Neural Networks (CNNs) have been proposed providing good performances. Recently, Vision Transformers (ViTs) introduced, reaching comparable even better performances than traditional CNNs image classification. aim paper was compare different state-of-the-art deep learning algorithms predict In work, using public database 144 patients, we implemented transfer approach, involving architectures like pre-trained ViTs, Pyramid Transformers, Swin from images, comparing their CNNs. Although, best study are reached via AUC, Accuracy, Sensitivity, Specificity, Precision equal 0.91, 0.89, 0.85, 0.90, 0.78, respectively, Transformer reach ones 0.86, 0.81, 0.75, respectively. Based on our preliminary experimental results, it appears that do not add improvements terms predictive performance addressed problem.

Language: Английский

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Vision Transformers: A Review of Architecture, Applications, and Future Directions

Published: Dec. 16, 2023

In recent years, the development of deep learning has revolutionized field computer vision, especially convolutional neural networks (CNNs), which become preferred approach for numerous tasks handling images. However, CNNs have difficulty interpreting massive and complicated datasets, led to creation alternative architectures such as vision transformers. The transformer architecture, was initially developed natural language processing (NLP), is modified image-related applications via this paper, we present an outline main concepts components We review various variations modifications compare different approaches based on their effectiveness, complexity, other attributes. Additionally, examine uses transformers, image classification, object detection, semantic segmentation, provide illustrations relevant real-world situations. Finally, discuss potential impact transformers while exploring challenges restrictions associated with usage. conclude by outlining new directions advancements in well areas that require further study investigation.

Language: Английский

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CECT: Controllable ensemble CNN and transformer for COVID-19 image classification

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 173, P. 108388 - 108388

Published: March 29, 2024

Language: Английский

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Vision Transformers-Based Transfer Learning for Breast Mass Classification From Multiple Diagnostic Modalities

Journal of Electrical Engineering and Technology, Journal Year: 2024, Volume and Issue: 19(5), P. 3391 - 3410

Published: April 5, 2024

Language: Английский

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Fusing global context with multiscale context for enhanced breast cancer classification

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

Published: Nov. 9, 2024

Breast cancer is the second most common type of among women. Prompt detection breast can impede its advancement to more advanced phases, thereby elevating probability favorable treatment consequences. Histopathological images are commonly used for classification due their detailed cellular information. Existing diagnostic approaches rely on Convolutional Neural Networks (CNNs) which limited local context resulting in a lower accuracy. Therefore, we present fusion model composed Vision Transformer (ViT) and custom Atrous Spatial Pyramid Pooling (ASPP) network with an attention mechanism effectively classifying from histopathological images. ViT enables attain global features, while ASPP accommodates multiscale features. Fusing features derived models resulted robust classifier. With help five-stage image preprocessing technique, proposed achieved 100% accuracy BreakHis dataset at 100X 400X magnification factors. On 40X 200X magnifications, 99.25% 98.26% respectively. commendable efficacy images, be considered dependable option proficient classification.

Language: Английский

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Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features

Breast Cancer Research and Treatment, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Language: Английский

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The Reliability of Deep Learning Models in Assessing the Shoulder Arthroscopic Field's Visual Clarity in Relation to Bleeding

JSES International, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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