# Medical Image Classification

Levit 128.fb Dist In1k Finetuned Stroke Binary
Apache-2.0
A vision Transformer model based on the LeViT-128 architecture, fine-tuned for binary stroke detection tasks
Image Classification Transformers
L
BTX24
18
1
Diabetic RetinoPathy Detection
Gpl-3.0
An image classification model fine-tuned based on Facebook's DINOv2 base model, specifically designed for diabetic retinopathy detection, achieving 96.8% accuracy on the evaluation set.
Image Classification Transformers Supports Multiple Languages
D
AsmaaElnagger
1,008
1
Beit Base Patch16 224 Pt22k Ft22k Finetuned Stroke Binary
Apache-2.0
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on a binary stroke detection dataset for image classification tasks, achieving an evaluation accuracy of 92.22%.
Image Classification Transformers
B
BTX24
36
1
Skin Disorders Classifier
An image classification model built with PyTorch and HuggingPics for identifying multiple common skin diseases.
Image Classification
S
sagarvk24
123
1
Path Foundation
Other
Path Foundation is a machine learning model for histopathology applications, trained through self-supervised learning to generate 384-dimensional embedding vectors from H&E-stained slides for efficient classifier model training.
Image Classification English
P
google
220
39
Medcsp Clip
MIT
A zero-shot medical image classification model based on the CLIP architecture
Text-to-Image
M
xcwangpsu
91
1
Skin Cancer Image Classification
Apache-2.0
Vision Transformer (ViT)-based skin cancer image classification model capable of identifying 7 types of skin lesions
Image Classification Transformers
S
Anwarkh1
3,309
22
Alzheimer MRI
Apache-2.0
A fine-tuned Alzheimer's disease MRI image classification model based on Google's ViT base model, achieving 92.6% accuracy
Image Classification Transformers
A
DHEIVER
354
2
Brain Tumor Detection
Apache-2.0
A brain tumor detection model based on the Swin Transformer architecture, achieving 98.04% accuracy in image classification tasks
Image Classification Transformers
B
ShimaGh
421
4
Medical
A medical image classification model generated using the PyTorch framework and HuggingPics tool, designed to identify different types of lung tissue images.
Image Classification Transformers
M
subh71
63
4
Skintelligent Acne
MIT
An image classification model for assessing acne severity, supporting 6-level classification from no acne to extremely severe acne
Image Classification Transformers English
S
imfarzanansari
484
13
Histo Train Vit
Apache-2.0
An image classification model fine-tuned based on google/vit-base-patch16-224, achieving an accuracy of 82.5% on the evaluation set
Image Classification Transformers
H
tcvrishank
18
0
Histo Train Swin
Apache-2.0
This model is a fine-tuned image classification model based on the Swin Transformer architecture, achieving 90% accuracy on the evaluation set.
Image Classification Transformers
H
tcvrishank
16
0
Histo Train Segformer
Other
Image classification model based on SegFormer architecture, fine-tuned on imagefolder dataset with 87.5% accuracy
Image Classification Transformers
H
tcvrishank
14
0
Autotrain Dementia Classification 41162106183
This is a multi-class image classification model trained via AutoTrain, specifically designed for dementia-related image classification tasks.
Image Classification Transformers
A
RiniPL
30
0
Ultrasound Lung
A multi-class classification model for lung ultrasound images trained with AutoTrain, featuring high accuracy
Image Classification Transformers
U
hamdan07
49
3
Octfusion Exp1 HKDB Synthetic
OCTFusion is a PyTorch-based image classification model that achieved 100% accuracy on synthetic data.
Image Classification Transformers
O
g30rv17ys
33
0
Octfusion Exp1 HKDB Unbalanced
OCTFusion is an image classification model based on PyTorch, achieving 80% accuracy on imbalanced datasets.
Image Classification Transformers
O
g30rv17ys
34
0
Autotrain Pneumo V3 3180589690
This is a binary classification model trained via AutoTrain for detecting pneumonia-related medical images.
Image Classification Transformers
A
ashutoshmondal
37
0
Brain Tumor Classification Using Swin Transformer
Apache-2.0
This model is a brain tumor image classification model based on the Swin Transformer architecture, achieving outstanding performance in image classification tasks with an accuracy rate of 99.49%.
Image Classification Transformers
B
surajjoshi
103
1
Vit Large Patch32 384 Melanoma
Apache-2.0
A melanoma image classification model fine-tuned based on Google's ViT-Large model, achieving 82.73% accuracy on the evaluation set
Image Classification Transformers
V
UnipaPolitoUnimore
100
1
Brain Tumor Classification
Apache-2.0
A fine-tuned brain tumor image classification model based on Swin Transformer architecture, achieving 96.47% accuracy on the evaluation set
Image Classification Transformers
B
Devarshi
205
9
Deit Base Mri
Apache-2.0
An image classification model fine-tuned on the mriDataSet dataset based on facebook/deit-base-distilled-patch16-224, achieving an accuracy of 99.01%
Image Classification Transformers
D
raedinkhaled
15
0
Swin Tiny Patch4 Window7 224 Finetuned Mri
Apache-2.0
This model is a fine-tuned version based on the Swin Transformer architecture, specifically designed for MRI image classification tasks, achieving an accuracy of 98.07% on the evaluation set.
Image Classification Transformers
S
raedinkhaled
14
1
Vit Base Xray Pneumonia
Apache-2.0
A ViT-based chest X-ray pneumonia classification model, fine-tuned on pneumonia dataset with 90.06% accuracy
Image Classification Transformers
V
nickmuchi
40
4
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