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 21–30 of 424 papers

Title	Date	Tasks	Status	Hype
Federated Learning for Medical Image Classification: A Comprehensive Benchmark	Apr 7, 2025	Computational EfficiencyDenoising	—Unverified	0
Diffusion models applied to skin and oral cancer classification	Mar 28, 2025	Binary ClassificationCancer Classification	—Unverified	0
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching	Mar 12, 2025	FairnessFederated Learning	CodeCode Available	1
Keeping Representation Similarity in Finetuning for Medical Image Analysis	Mar 10, 2025	image-classificationImage Classification	—Unverified	0
XFMamba: Cross-Fusion Mamba for Multi-View Medical Image Classification	Mar 4, 2025	Classificationimage-classification	CodeCode Available	1
AI-Augmented Thyroid Scintigraphy for Robust Classification	Mar 1, 2025	ClassificationData Augmentation	—Unverified	0
MedKAN: An Advanced Kolmogorov-Arnold Network for Medical Image Classification	Feb 25, 2025	image-classificationImage Classification	—Unverified	0
Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?	Feb 24, 2025	ClassificationDiversity	CodeCode Available	0
Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attention	Feb 19, 2025	image-classificationImage Classification	CodeCode Available	2
Benchmarking MedMNIST dataset on real quantum hardware	Feb 18, 2025	Benchmarkingimage-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