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 411–420 of 424 papers

Title	Date	Tasks	Status
Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks	Mar 25, 2020	image-classificationImage Classification	—Unverified
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis	Mar 4, 2022	Fairnessimage-classification	—Unverified
FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classification	Dec 20, 2024	DiagnosticDisentanglement	—Unverified
CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models	Feb 4, 2025	Adversarial AttackDiagnostic	—Unverified
Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification	Sep 6, 2022	image-classificationImage Classification	—Unverified
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior	May 23, 2025	image-classificationImage Classification	—Unverified
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis	Jul 2, 2024	ClassificationFederated Learning	—Unverified
Federated Learning for Medical Image Classification: A Comprehensive Benchmark	Apr 7, 2025	Computational EfficiencyDenoising	—Unverified
CopilotCAD: Empowering Radiologists with Report Completion Models and Quantitative Evidence from Medical Image Foundation Models	Apr 11, 2024	Diagnosticimage-classification	—Unverified
Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection	Jan 5, 2023	image-classificationImage Classification	—Unverified

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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