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 141–150 of 424 papers

Title	Date	Tasks	Status
Assessing Robustness to Noise: Low-Cost Head CT Triage	Mar 17, 2020	Computed Tomography (CT)image-classification	—Unverified
Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification	Jul 24, 2023	Drug DiscoveryGraph Attention	—Unverified
Aggregative Self-Supervised Feature Learning from a Limited Sample	Dec 14, 2020	image-classificationImage Classification	—Unverified
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization	Oct 9, 2020	Breast Cancer DetectionDecision Making	—Unverified
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification	Dec 14, 2020	General Classificationimage-classification	—Unverified
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification	Mar 2, 2023	ClusteringDomain Adaptation	—Unverified
CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification	May 3, 2025	image-classificationImage Classification	—Unverified
A Fully Convolutional Normalization Approach of Head and Neck Cancer Outcome Prediction	Jan 25, 2020	image-classificationImage Classification	—Unverified
Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training	Jan 16, 2024	image-classificationImage Classification	—Unverified
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification	Oct 4, 2022	Active LearningClassification	—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