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 391–400 of 424 papers

Title	Date	Tasks	Status
Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks	Mar 25, 2020	image-classificationImage Classification	—Unverified
Assessing Robustness to Noise: Low-Cost Head CT Triage	Mar 17, 2020	Computed Tomography (CT)image-classification	—Unverified
The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification	Mar 13, 2020	AllClassification	—Unverified
Point-of-Care Diabetic Retinopathy Diagnosis: A Standalone Mobile Application Approach	Jan 26, 2020	Deep LearningDiagnostic	—Unverified
A Fully Convolutional Normalization Approach of Head and Neck Cancer Outcome Prediction	Jan 25, 2020	image-classificationImage 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
Visualization approach to assess the robustness of neural networks for medical image classification	Nov 19, 2019	General Classificationimage-classification	—Unverified
MixModule: Mixed CNN Kernel Module for Medical Image Segmentation	Oct 19, 2019	image-classificationImage Classification	—Unverified
Joint Acne Image Grading and Counting via Label Distribution Learning	Oct 1, 2019	Acne Severity GradingMedical Image Classification	CodeCode Available
Multi-Instance Multi-Scale CNN for Medical Image Classification	Jul 4, 2019	ClassificationGeneral 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