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 341–350 of 424 papers

Title	Date	Tasks	Status
Improving the repeatability of deep learning models with Monte Carlo dropout	Feb 15, 2022	AttributeBinary Classification	CodeCode Available
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images	Aug 22, 2018	Computational EfficiencyGeneral Classification	CodeCode Available
Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining	Sep 29, 2023	Diagnosticimage-classification	CodeCode Available
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation	Feb 20, 2018	image-classificationImage Classification	CodeCode Available
Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging	Dec 30, 2024	Binary ClassificationClassification	CodeCode Available
LMFLOSS: A Hybrid Loss For Imbalanced Medical Image Classification	Dec 24, 2022	Classificationimage-classification	CodeCode Available
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output Codes	May 9, 2025	Classificationimage-classification	CodeCode Available
Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier Calibration	Feb 5, 2025	Classifier calibrationDiagnostic	CodeCode Available
Homogeneous Learning: Self-Attention Decentralized Deep Learning	Oct 11, 2021	Deep LearningFederated Learning	CodeCode Available
Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing	Apr 21, 2022	Contrastive LearningLanguage Modeling	CodeCode Available

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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