This project focuses on the classification of images into two categories: those containing melanoma, a type of skin cancer, and those that do not. The classification is achieved using Convolutional Neural Networks (CNNs) trained on image data obtained from Kaggle.
Melanoma is a serious form of skin cancer, and early detection is crucial for successful treatment. This project aims to develop a model capable of accurately identifying melanoma in images, which could assist dermatologists and medical professionals in diagnosis.
The project consists of the following components:
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Data Preprocessing: The
data_preprocessing.ipynbnotebook contains code for preprocessing the image data, including loading, shuffling, normalization, and splitting into training, validation, and testing sets. -
CNN Model: The
CNN_model.ipynbnotebook implements the CNN architecture for image classification. It defines the neural network architecture, trains the model on the preprocessed data, and evaluates its performance.
To use this project:
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Download Dataset: Download the melanoma image dataset from Kaggle.
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Data Preparation: Organize the dataset into appropriate directories (
../data/trainand../data/test). -
Data Preprocessing: Execute the
data_preprocessing.ipynbnotebook to preprocess the data, including loading, shuffling, and splitting. -
Model Training: Run the
CNN_model.ipynbnotebook to define, train, and evaluate the CNN model for image classification. -
Evaluation and Analysis: Analyze the model's performance using metrics such as loss curves, accuracy, classification reports, and confusion matrices.
Ensure you have the following dependencies installed to run the notebooks:
- Python (>=3.6)
- PyTorch
- torchvision
- NumPy
- Matplotlib
- Seaborn
- scikit-learn