Autoencoder_UMAPSkin Pathology Image Classification

This is a Python program for classifying skin pathology images using deep learning and UMAP visualization. The program takes a set of skin pathology images, preprocesses them, trains a convolutional neural network on the data, and then applies UMAP dimensionality reduction to visualize the learned features in two dimensions. The resulting 2D embedding can be used to visualize the relationships between the different classes of skin pathology images and gain insight into the learned representations. ** Requirements** The following packages are required to run this program:

Python 3 Tensorflow 2 Keras UMAP Scikit-learn Matplotlib OpenCV You can install these packages by running the following command: pip install tensorflow keras umap-learn scikit-learn matplotlib opencv-python-headless Usage

1. Place your skin pathology images in a folder called images in the root directory of the project.
2. Run the program by executing the skin_pathology_classification.py script.
3. Wait for the program to preprocess the data, train the neural network, and apply UMAP dimensionality reduction to visualize the learned features.
4. The resulting 2D embedding will be displayed in a Matplotlib window.

Code The skin_pathology_classification.py script contains the main code for this program. It is divided into the following sections:

1. Importing the required packages and defining constants.
2. Loading and preprocessing the skin pathology images.
3. Splitting the data into training and testing sets.
4. Defining and training the convolutional neural network.
5. Applying UMAP dimensionality reduction to visualize the learned features.
6. Plotting the 2D embedding.

Conclusion This program provides an example of how to use deep learning and UMAP visualization to classify and visualize skin pathology images. The resulting 2D embedding can be used to explore the relationships between different classes of skin pathology images and gain insight into the learned representations.