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

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Showing 381–390 of 424 papers

Title	Date	Tasks	Status
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network	Jun 14, 2018	Active LearningGeneral Classification	—Unverified
Efficient feature embedding of 3D brain MRI images for content-based image retrieval with deep metric learning	Dec 4, 2019	ClusteringContent-Based Image Retrieval	—Unverified
Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning	Jan 16, 2025	Contrastive LearningFew-Shot Learning	—Unverified
Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images	Aug 1, 2024	Brain Tumor ClassificationDecision Making	—Unverified
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis	Oct 12, 2024	Image ClassificationMedical Image Analysis	—Unverified
Adversarial Attacks and Defences for Skin Cancer Classification	Dec 13, 2022	Adversarial AttackAdversarial Defense	—Unverified
Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification	Apr 15, 2025	image-classificationImage Classification	—Unverified
Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis	Dec 12, 2024	AllClassification	—Unverified
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning	Jun 10, 2020	ClusteringGeneral Classification	—Unverified
Semi-supervised learning for medical image classification using imbalanced training data	Aug 20, 2021	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