Medical Image Classification

Medical Image Classification is a task in medical image analysis that involves classifying medical images, such as X-rays, MRI scans, and CT scans, into different categories based on the type of image or the presence of specific structures or diseases. The goal is to use computer algorithms to automatically identify and classify medical images based on their content, which can help in diagnosis, treatment planning, and disease monitoring.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 81–90 of 424 papers

Title	Date	Tasks	Status	Hype
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation	Aug 11, 2024	Benchmarkingimage-classification	—Unverified	0
Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration	Aug 6, 2024	Classificationimage-classification	—Unverified	0
Boosting Memory Efficiency in Transfer Learning for High-Resolution Medical Image Classification	Aug 5, 2024	image-classificationImage Classification	CodeCode Available	1
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder	Aug 2, 2024	counterfactualimage-classification	CodeCode Available	1
Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images	Aug 1, 2024	Brain Tumor ClassificationDecision Making	—Unverified	0
UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks	Jul 29, 2024	Federated Learningimage-classification	CodeCode Available	0
Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?	Jul 24, 2024	Classificationgraph construction	CodeCode Available	0
Addressing Imbalance for Class Incremental Learning in Medical Image Classification	Jul 18, 2024	class-incremental learningClass Incremental Learning	—Unverified	0
Non-parametric regularization for class imbalance federated medical image classification	Jul 17, 2024	Federated Learningimage-classification	CodeCode Available	0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification	Jul 16, 2024	Federated Learningimage-classification	—Unverified	0

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All datasets NCT-CRC-HE-100K IDRiD ImageNet PCOS Classification CheXphoto Galaxy10 DECals ISIC 2017 ISIC 2020 Challenge Dataset Malaria Dataset OASIS 3

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Efficientnet-b0	Accuracy (%)	95.59	—	Unverified
2	ResNeXt-50-32x4d	Accuracy (%)	95.46	—	Unverified
3	RegNetY-3.2GF	Accuracy (%)	95.42	—	Unverified
4	ResNet-50	Accuracy (%)	94.72	—	Unverified
5	DenseNet-169	Accuracy (%)	94.41	—	Unverified
6	Res2Net-50	Accuracy (%)	93.37	—	Unverified
7	ResNet-18	Accuracy (%)	92.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-152	Accuracy (% )	86.56	—	Unverified
2	Beta-Rank	Accuracy	81.88	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DaViT-S	GFLOPs	8.8	—	Unverified
2	DaViT-T	GFLOPs	4.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	InceptionV3	1:1 Accuracy	90.2	—	Unverified
2	EfficientNet B7	1:1 Accuracy	88.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PTRN	Mean AUC	0.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Astroformer	Top-1 Accuracy (%)	94.87	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Beta-Rank	Accuracy	72.44	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EfficientNet Ensemble	AUC	0.95	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SNAPSHOT ENSEMBLE	F1 score	99.37	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3D CNN	AUC	87	—	Unverified