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 281–290 of 424 papers

Title	Date	Tasks	Status
Privacy-preserving Machine Learning for Medical Image Classification	Aug 29, 2021	BIG-bench Machine LearningClassification	—Unverified
Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition	Aug 31, 2023	image-classificationImage Classification	—Unverified
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification	Jul 16, 2024	Federated Learningimage-classification	—Unverified
RadTex: Learning Efficient Radiograph Representations from Text Reports	Aug 5, 2022	ClassificationDecoder	—Unverified
Reconstructing Images of Two Adjacent Objects through Scattering Medium Using Generative Adversarial Network	Jul 24, 2021	Generative Adversarial Networkimage-classification	—Unverified
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST	Jan 24, 2025	Classificationimage-classification	—Unverified
Review of AlexNet for Medical Image Classification	Nov 15, 2023	Classificationimage-classification	—Unverified
A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers	Mar 13, 2023	Binary ClassificationDecoder	—Unverified
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models	Oct 4, 2023	Diagnosticimage-classification	—Unverified
Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification	Jan 23, 2019	General 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