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 111–120 of 424 papers

Title	Date	Tasks	Status
Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise	Nov 29, 2024	image-classificationImage Classification	—Unverified
Data Augmentation using Feature Generation for Volumetric Medical Images	Sep 28, 2022	ClassificationData Augmentation	—Unverified
Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks	Mar 25, 2020	image-classificationImage Classification	—Unverified
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities	Nov 1, 2021	Classificationimage-classification	—Unverified
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning	Feb 10, 2016	General Classificationimage-classification	—Unverified
Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data	Dec 29, 2024	Classificationimage-classification	—Unverified
Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images	Aug 1, 2024	Brain Tumor ClassificationDecision Making	—Unverified
Deep learning pipeline for image classification on mobile phones	May 31, 2022	ClassificationDeep Learning	—Unverified
A Lightweight Neural Architecture Search Model for Medical Image Classification	May 6, 2024	Deep Learningimage-classification	—Unverified
CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models	Feb 4, 2025	Adversarial AttackDiagnostic	—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