https://github.com/redayzarra/ml-skincancer-project
This project utilizes a convolutional network to identify 9 different kinds of skin cancers including melanoma, nevus, and more. The model is trained on over 2,200 pictures of various skin cancers based off of this dataset. This model implements fundamental computer vision and classification techniques and includes a step-by-step implementation.
https://github.com/redayzarra/ml-skincancer-project
computer-vision convolutional-neural-networks data-visualization deep-learning lenet lenet-5 lenet-architecture machine-learning machine-learning-algorithms neural-network
Last synced: 2 months ago
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This project utilizes a convolutional network to identify 9 different kinds of skin cancers including melanoma, nevus, and more. The model is trained on over 2,200 pictures of various skin cancers based off of this dataset. This model implements fundamental computer vision and classification techniques and includes a step-by-step implementation.
- Host: GitHub
- URL: https://github.com/redayzarra/ml-skincancer-project
- Owner: redayzarra
- Created: 2023-01-12T01:32:10.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2023-01-27T21:10:56.000Z (over 3 years ago)
- Last Synced: 2025-07-19T18:03:48.768Z (11 months ago)
- Topics: computer-vision, convolutional-neural-networks, data-visualization, deep-learning, lenet, lenet-5, lenet-architecture, machine-learning, machine-learning-algorithms, neural-network
- Language: Jupyter Notebook
- Homepage:
- Size: 5.15 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Skin Cancer Classification - Convolutional Network
## Overview - Active Project
This project utilizes a convolutional network to identify 9 different kinds of skin cancers including melanoma, nevus, and more. The model is trained on over 2,200 pictures of various skin cancers based off of this [dataset](https://www.kaggle.com/datasets/nodoubttome/skin-cancer9-classesisic). This model implements fundamental computer vision and classification techniques and includes a step-by-step implementation of the model as well as in-depth notes to customize the model further for higher accuracy.
## Project Website
If you would like to find out more about the project, please checkout: [Project Website](https://www.redaysblog.com/)
## Installing the libraries
This project uses several important libraries such as Pandas, NumPy, Matplotlib, and more. You can install them all by running the following commands with pip:
```bash
pip install pandas
pip install numpy
python -m pip install -U matplotlib
pip install seaborn
pip install -U scikit-learn
pip install tensorflow
```
If you are not able to install the necessary libraries, I recommend you **use Jupyter Notebook with Anaconda**. I have a .ipynb file for the project as well.
## Configurations
This project utilizes a CSV file for loading the dataset. If you have a CSV file full of text that you would like to use, please feel free to use this code to load your dataset in to the file:
```python
with open("YOUR-TRAINING-DATA.p", mode = 'rb') as training_data:
train = pickle.load(training_data)
with open("YOUR-VALIDATION-DATA.p", mode = 'rb') as validation_data:
valid = pickle.load(validation_data)
with open("YOUR-TEST-DATA.p", mode = 'rb') as testing_data:
test = pickle.load(testing_data)
```
## License
[MIT](https://choosealicense.com/licenses/mit/)