# Medical Image Classification

A vision Transformer model based on the LeViT-128 architecture, fine-tuned for binary stroke detection tasks

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.

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%.

An image classification model built with PyTorch and HuggingPics for identifying multiple common skin diseases.

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.

A zero-shot medical image classification model based on the CLIP architecture

Vision Transformer (ViT)-based skin cancer image classification model capable of identifying 7 types of skin lesions

A fine-tuned Alzheimer's disease MRI image classification model based on Google's ViT base model, achieving 92.6% accuracy

A brain tumor detection model based on the Swin Transformer architecture, achieving 98.04% accuracy in image classification tasks

A medical image classification model generated using the PyTorch framework and HuggingPics tool, designed to identify different types of lung tissue images.

An image classification model for assessing acne severity, supporting 6-level classification from no acne to extremely severe acne

An image classification model fine-tuned based on google/vit-base-patch16-224, achieving an accuracy of 82.5% on the evaluation set

This model is a fine-tuned image classification model based on the Swin Transformer architecture, achieving 90% accuracy on the evaluation set.

Image classification model based on SegFormer architecture, fine-tuned on imagefolder dataset with 87.5% accuracy

This is a multi-class image classification model trained via AutoTrain, specifically designed for dementia-related image classification tasks.

A multi-class classification model for lung ultrasound images trained with AutoTrain, featuring high accuracy

OCTFusion is a PyTorch-based image classification model that achieved 100% accuracy on synthetic data.

OCTFusion is an image classification model based on PyTorch, achieving 80% accuracy on imbalanced datasets.

This is a binary classification model trained via AutoTrain for detecting pneumonia-related medical images.

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%.

A melanoma image classification model fine-tuned based on Google's ViT-Large model, achieving 82.73% accuracy on the evaluation set

A fine-tuned brain tumor image classification model based on Swin Transformer architecture, achieving 96.47% accuracy on the evaluation set

An image classification model fine-tuned on the mriDataSet dataset based on facebook/deit-base-distilled-patch16-224, achieving an accuracy of 99.01%

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.

A ViT-based chest X-ray pneumonia classification model, fine-tuned on pneumonia dataset with 90.06% accuracy