https://github.com/anwai98/skin-lesion

Diagnosis of Dermoscopic Images using Multi-Sizing Ensemble-Based Deep Learning Method
https://github.com/anwai98/skin-lesion

classification computer-aided-diagnosis deep-learning ham10000 melanoma pytorch skin-lesion-classification

Last synced: 9 days ago
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Diagnosis of Dermoscopic Images using Multi-Sizing Ensemble-Based Deep Learning Method

• Host: GitHub
• URL: https://github.com/anwai98/skin-lesion
• Owner: anwai98
• Created: 2022-01-31T06:58:43.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-04-27T12:36:40.000Z (over 2 years ago)
• Last Synced: 2024-10-20T14:28:39.710Z (2 months ago)
• Topics: classification, computer-aided-diagnosis, deep-learning, ham10000, melanoma, pytorch, skin-lesion-classification
• Language: Jupyter Notebook
• Homepage:
• Size: 639 KB
• Stars: 0
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Diagnosis of Dermoscopic Images using Multi-Sizing Ensemble-Based Deep Learning Method

Link for the Presentation - https://docs.google.com/presentation/d/1FBZ0tmoJJjrAS50cO-lpZKxSRPU5a5Ka7sM7yRlz8rs/edit?usp=sharing

To View the Validation Results, Have a Look at the Presentation.

How to Run the Code?

1. Train the Architectures with your Preferences and/or Requirements, or better Devise your Own using the Referred PyTorch Example.

2. Play with the Hyperparameters and Different Architecture Configurations. It is my favourite part.

3. Have Fun Transfering/Learning the Learnings. 

4. Dataset - https://doi.org/10.7910/DVN/DBW86T

> Note : Comments are provided for easier explanation. For more information, contact me!

## Challenge Results (MAIA - 3rd Semester)

Our Methodology won the 1st Place in both the Binary with 90.8% Accuracy and Multi-Class 92% Accuracy (0.857 Kappa Score) on the Test Dataset in the Deep Learning Based Classification Challenges.