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 191–200 of 424 papers

Title	Date	Tasks	Status
Deep reinforced active learning for multi-class image classification	Jun 20, 2022	Active LearningClassification	—Unverified
Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification	Sep 28, 2022	feature selectionimage-classification	—Unverified
An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images	Sep 24, 2024	image-classificationImage Classification	—Unverified
Deep learning pipeline for image classification on mobile phones	May 31, 2022	ClassificationDeep Learning	—Unverified
Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images	Aug 1, 2024	Brain Tumor ClassificationDecision Making	—Unverified
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations	Jan 20, 2021	Cancer-no cancer per image classificationGPU	—Unverified
Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data	Dec 29, 2024	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
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification	Dec 10, 2022	Decision MakingExplainable artificial intelligence	—Unverified
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities	Nov 1, 2021	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