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Transfer Learning Techniques in Medical Image Classification
Medical image classification is a critical task in modern health care that aids in diagnosis, treatment planning, and patient care. However, it is...
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Medical image classification for Alzheimer’s using a deep learning approach
Medical image categorization is essential for a variety of medical assessments and education functions. The purpose of medical image classification...
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A stereo spatial decoupling network for medical image classification
Deep convolutional neural network (CNN) has made great progress in medical image classification. However, it is difficult to establish effective...
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Illumination robust deep convolutional neural network for medical image classification
In the past few years, the performance of deep learning is quite recognizable in the biomedical image processing domain. The convolutional neural...
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Hybrid Neural Networks for Medical Image Classification
The paper explores the integration of artificial intelligence (AI) methods, particularly neural networks, and quantum computing for medical...
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State-of-Art Review on Medical Image Classification Techniques
Medical image is made of a pixel which shows a real-world object. In terms of understanding their relevance for insight, analysis and disease...
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A multi-label image classification method combining multi-stage image semantic information and label relevance
Multi-label image classification (MLIC) is a fundamental and highly challenging task in the field of computer vision. Most methods usually only focus...
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Improving Medical Image Classification in Noisy Labels Using only Self-supervised Pretraining
Noisy labels hurt deep learning-based supervised image classification performance as the models may overfit the noise and learn corrupted feature...
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Arithmetic Optimization Algorithm with Deep Learning-Based Medical X-Ray Image Classification Model
Recently, number of medical X-ray images being generated is increasing rapidly due to the advancements in radiological equipment in medical centres....
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Topological data analysis and image visibility graph for texture classification
Texture, a crucial element in image recognition, presents challenges in computer vision tasks like segmentation and classification. This study aims...
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Advantages of transformer and its application for medical image segmentation: a survey
PurposeConvolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network...
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TwT: A Texture weighted Transformer for Medical Image Classification and Diagnosis
Medical imaging is an integral part of disease diagnosis and treatment. However, interpreting medical images can be time-consuming and subjective,...
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Positional Information is a Strong Supervision for Volumetric Medical Image Segmentation
Medical image segmentation is a crucial preliminary step for a number of downstream diagnosis tasks. As deep convolutional neural networks...
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Self-supervised learning for medical image analysis: a comprehensive review
Deep learning and advancements in computer vision offer significant potential for analyzing medical images resulting in better healthcare and...
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A distribution information sharing federated learning approach for medical image data
In recent years, federated learning has been believed to play a considerable role in cross-silo scenarios (e.g., medical institutions) due to its...
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Intelligent biomedical image classification in a big data architecture using metaheuristic optimization and gradient approximation
Medical imaging has experienced significant development in contemporary medicine and can now record a variety of biomedical pictures from patients to...
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FedCNNAvg: Federated Learning for Preserving-Privacy of Multi-clients Decentralized Medical Image Classification
Federated Learning (FL) permits the cooperative training of a joint model for several medical facilities while maintaining the decentralization of...
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Exploring the features of quanvolutional neural networks for improved image classification
Image classification has an important role in many machine learning applications. Numerous classification techniques based on quantum machine...
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Enhancement of Few-shot Image Classification Using Eigenimages
In this paper, we propose an auxiliary loss function called an eigen loss to reduce the overfitting of few-shot learning algorithms. The proposed...
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Unified deep learning model for multitask representation and transfer learning: image classification, object detection, and image captioning
The application of deep learning has demonstrated impressive performance in computer vision tasks such as object detection, image classification, and...
