https://github.com/saranshmanu/dermatologist-ai
In this mini project, I will design an algorithm that can visually diagnose melanoma, the deadliest form of skin cancer. In particular, algorithm will distinguish this malignant skin tumor from two types of benign lesions (nevi and seborrheic keratoses).
https://github.com/saranshmanu/dermatologist-ai
cancer-detection cnn transfer-learning vgg19
Last synced: about 2 months ago
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In this mini project, I will design an algorithm that can visually diagnose melanoma, the deadliest form of skin cancer. In particular, algorithm will distinguish this malignant skin tumor from two types of benign lesions (nevi and seborrheic keratoses).
- Host: GitHub
- URL: https://github.com/saranshmanu/dermatologist-ai
- Owner: saranshmanu
- Created: 2018-07-22T22:01:17.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-05-28T20:47:27.000Z (almost 6 years ago)
- Last Synced: 2025-02-01T07:19:11.769Z (4 months ago)
- Topics: cancer-detection, cnn, transfer-learning, vgg19
- Language: Jupyter Notebook
- Homepage:
- Size: 37.5 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Dermatologist-AI
## Introduction
In this mini project, I will design an algorithm that can visually diagnose melanoma, the deadliest form of skin cancer. In particular, algorithm will distinguish this malignant skin tumor from two types of benign lesions (nevi and seborrheic keratoses).
The data and objective are pulled from the 2017 ISIC Challenge on Skin Lesion Analysis Towards Melanoma Detection. As part of the challenge, I was tasked to design an algorithm to diagnose skin lesion images as one of three different skin diseases (melanoma, nevus, or seborrheic keratosis). In this project, I will create a model to generate your own predictions.
### Framewords
- Keras
- Numpy
- Pandas
### Resources