Advances in medical image analysis with vision Transformers: A comprehensive review

Medical Image Analysis, Journal Year: 2023, Volume and Issue: 91, P. 103000 - 103000

Published: Oct. 19, 2023

Language: Английский

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Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images

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

Published: Jan. 7, 2025

In the present scenario, cancerous tumours are common in humans due to major changes nearby environments. Skin cancer is a considerable disease detected among people. This uncontrolled evolution of atypical skin cells. It occurs when DNA injury cells, or genetic defect, leads an increase quickly and establishes malignant tumors. However, rare instances, many types occur from tempted by infrared light affecting worldwide health problem, so accurate appropriate diagnosis needed for efficient treatment. Current developments medical technology, like smart recognition analysis utilizing machine learning (ML) deep (DL) techniques, have transformed treatment these conditions. These approaches will be highly effective biomedical imaging. study develops Multi-scale Feature Fusion Deep Convolutional Neural Networks on Cancerous Tumor Detection Classification (MFFDCNN-CTDC) model. The main aim MFFDCNN-CTDC model detect classify using To eliminate unwanted noise, method initially utilizes sobel filter (SF) image preprocessing stage. For segmentation process, Unet3+ employed, providing precise localization tumour regions. Next, incorporates multi-scale feature fusion combining ResNet50 EfficientNet architectures, capitalizing their complementary strengths extraction varying depths scales input images. convolutional autoencoder (CAE) utilized classification method. Finally, parameter tuning process performed through hybrid fireworks whale optimization algorithm (FWWOA) enhance performance CAE A wide range experiments authorize approach. experimental validation approach exhibited superior accuracy value 98.78% 99.02% over existing techniques under ISIC 2017 HAM10000 datasets.

Language: Английский

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A Local-Global Attention Fusion Framework with Tensor Decomposition for Medical Diagnosis

IEEE/CAA Journal of Automatica Sinica, Journal Year: 2024, Volume and Issue: 11(6), P. 1536 - 1538

Published: May 27, 2024

Dear Editor, In this letter, a novel hierarchical fusion framework is proposed to address the imperfect data property in complex medical image analysis (MIA) scenes. particular, by combining strengths of convolutional neural networks (CNNs) and transformers, enhanced feature extraction, spatial modeling, sequential context learning are realized provide comprehensive insights on patterns. Integration information different level enabled via multi-attention mechanism, tensor decomposition methods adopted so that compact distinctive representation underlying high-dimensional features can be accomplished [1]. It shown from evaluation results competitive superior as compared with some other advanced algorithms, which effectively handles inter-class similarity intra-class differences diseases, meanwhile, model complexity reduced within an acceptable level, benefits deployment clinic practice.

Language: Английский

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DAX-Net: A dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images

Computer Methods and Programs in Biomedicine, Journal Year: 2024, Volume and Issue: 248, P. 108112 - 108112

Published: March 7, 2024

Multi-class cancer classification has been extensively studied in digital and computational pathology due to its importance clinical decision-making. Numerous tools have proposed for various types of classification. Many them are built based on convolutional neural networks. Recently, Transformer-style networks shown be effective Herein, we present a hybrid design that leverages both transformer architecture obtain superior performance

Language: Английский

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ASFESRN: bridging the gap in real-time corn leaf disease detection with image super-resolution

Multimedia Systems, Journal Year: 2024, Volume and Issue: 30(4)

Published: June 14, 2024

Language: Английский

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Optimized deep learning model for comprehensive medical image analysis across multiple modalities

Neurocomputing, Journal Year: 2024, Volume and Issue: 619, P. 129182 - 129182

Published: Dec. 12, 2024

Language: Английский

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Multi-style spatial attention module for cortical cataract classification in AS-OCT image with supervised contrastive learning

Computer Methods and Programs in Biomedicine, Journal Year: 2023, Volume and Issue: 244, P. 107958 - 107958

Published: Nov. 30, 2023

Language: Английский

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Gradient Guided Multiscale Feature Collaboration Networks for Few-Shot Class-Incremental Remote Sensing Scene Classification

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2024, Volume and Issue: 62, P. 1 - 12

Published: Jan. 1, 2024

Language: Английский

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Semantic Segmentation of Aerial Imagery: A Novel Approach Leveraging Hierarchical Multi-scale Features and Channel-based Attention for Drone Applications

International journal of engineering. Transactions B: Applications, Journal Year: 2024, Volume and Issue: 37(5), P. 1022 - 1035

Published: Jan. 1, 2024

Drone semantic segmentation is a challenging task in computer vision, mainly due to inherent complexities associated with aerial imagery. This paper presents comprehensive methodology for drone and evaluates its performance using the ICG dataset. The proposed method leverages hierarchical multi-scale feature extraction efficient channel-based attention Atrous Spatial Pyramid Pooling (ASPP) address unique challenges encountered this domain. In study, of compared several state-of-the-art models. findings research highlight effectiveness tackling segmentation. outcomes demonstrate superiority over models, showcasing potential accurate results contribute advancement drone-based applications, such as surveillance, object tracking, environmental monitoring, where precise crucial. obtained experimental that outperforms these existing approaches regarding Dice, mIOU, accuracy metrics. Specifically, achieves an impressive scores 86.51%, 76.23%, 91.74%, respectively.

Language: Английский

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MM-HiFuse: multi-modal multi-task hierarchical feature fusion for esophagus cancer staging and differentiation classification

Complex & Intelligent Systems, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 1, 2025

Language: Английский

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