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 311–320 of 424 papers

Title	Date	Tasks	Status
OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms	Aug 11, 2022	image-classificationImage Classification	—Unverified
RadTex: Learning Efficient Radiograph Representations from Text Reports	Aug 5, 2022	ClassificationDecoder	—Unverified
A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans	Aug 4, 2022	ClassificationComputed Tomography (CT)	—Unverified
Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence	Aug 3, 2022	Acne Severity GradingMedical Image Classification	CodeCode Available
Texture features in medical image analysis: a survey	Aug 2, 2022	image-classificationImage Classification	—Unverified
Information Gain Sampling for Active Learning in Medical Image Classification	Aug 1, 2022	Active LearningClassification	—Unverified
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification	Jul 14, 2022	Classificationimage-classification	—Unverified
PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification	Jul 11, 2022	image-classificationImage Classification	—Unverified
Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification	Jun 27, 2022	DisentanglementDomain Adaptation	—Unverified
A novel adversarial learning strategy for medical image classification	Jun 23, 2022	ClassificationGenerative Adversarial Network	—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