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 291–300 of 424 papers

Title	Date	Tasks	Status
Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection	Jan 5, 2023	image-classificationImage Classification	—Unverified
On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations	Jan 4, 2023	Fairnessimage-classification	CodeCode Available
A New Perspective to Boost Vision Transformer for Medical Image Classification	Jan 3, 2023	Diabetic Retinopathy Gradingimage-classification	—Unverified
PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training	Jan 1, 2023	image-classificationImage Classification	CodeCode Available
Chest X-Ray Images Classification with CNN	Dec 31, 2022	ClassificationDiagnostic	CodeCode Available
LMFLOSS: A Hybrid Loss For Imbalanced Medical Image Classification	Dec 24, 2022	Classificationimage-classification	CodeCode Available
Adversarial Attacks and Defences for Skin Cancer Classification	Dec 13, 2022	Adversarial AttackAdversarial Defense	—Unverified
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification	Dec 10, 2022	Decision MakingExplainable artificial intelligence	—Unverified
Vision Transformers in Medical Imaging: A Review	Nov 18, 2022	DecoderDiversity	—Unverified
Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays	Nov 8, 2022	image-classificationImage Classification	CodeCode Available

Show:10 25 50

← PrevPage 30 of 43Next →

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