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Melanoma Cancer Image Classification
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Melanoma Cancer Image Classification

Developed by Heem2
Fine-tuned based on Google's ViT model for melanoma cancer image classification, achieving an accuracy of 93.95%.
Image Classification
Transformers
Open Source License:Apache-2.0#Skin Cancer Classification#High-Precision ViT#Medical Image Diagnosis
Downloads 135
Release Time : 5/29/2024

Model Overview

This model is a fine-tuned version of google/vit-base-patch16-224-in21k, specifically designed for melanoma cancer image classification tasks. It performs excellently on the evaluation set with an accuracy of 93.95%.

Model Features

High Accuracy
Achieved an outstanding accuracy of 93.95% on the evaluation set.
Based on ViT Architecture
Utilizes the Vision Transformer architecture, which offers powerful image processing capabilities.
Fine-Tuned Optimization
Fine-tuned on the base model, specifically optimized for melanoma cancer image classification tasks.

Model Capabilities

Image Classification
Melanoma Detection

Use Cases

Medical Diagnosis
Skin Cancer Screening
Assists doctors in early screening and diagnosis of skin cancer.
With an accuracy rate as high as 93.95%, it can effectively aid in diagnosis.
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