Melanoma Image Classification Project

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.

Overview

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.

Project Structure

The project consists of the following components:

1. Data Preprocessing: The data_preprocessing.ipynb notebook contains code for preprocessing the image data, including loading, shuffling, normalization, and splitting into training, validation, and testing sets.

2. CNN Model: The CNN_model.ipynb notebook implements the CNN architecture for image classification. It defines the neural network architecture, trains the model on the preprocessed data, and evaluates its performance.

Usage

To use this project:

1. Download Dataset: Download the melanoma image dataset from Kaggle.

2. Data Preparation: Organize the dataset into appropriate directories (../data/train and ../data/test).

3. Data Preprocessing: Execute the data_preprocessing.ipynb notebook to preprocess the data, including loading, shuffling, and splitting.

4. Model Training: Run the CNN_model.ipynb notebook to define, train, and evaluate the CNN model for image classification.

5. Evaluation and Analysis: Analyze the model's performance using metrics such as loss curves, accuracy, classification reports, and confusion matrices.

Dependencies

Ensure you have the following dependencies installed to run the notebooks:

• Python (>=3.6)
• PyTorch
• torchvision
• NumPy
• Matplotlib
• Seaborn
• scikit-learn