** Skin Disease Classification Project **
Overview This project aims to classify skin diseases using deep learning techniques with TensorFlow/Keras. It involves loading a dataset of skin disease images, building a convolutional neural network (CNN) model, training it on the dataset, and evaluating its performance.
Dataset The dataset used in this project consists of images depicting various types of skin diseases. It includes classes such as melanoma, seborrheic keratosis, and others. The dataset is preprocessed using techniques like resizing, normalization, and augmentation to enhance model performance.
Model Architecture The CNN model architecture is designed to extract meaningful features from the skin disease images. It includes convolutional layers for feature extraction, batch normalization layers for improving convergence speed and stability, max-pooling layers for spatial downsampling, and dropout layers for regularization to prevent overfitting.
Training The model is trained on a split dataset, with a training set and a validation set. During training, the model learns to classify skin disease images into their respective classes. The training process involves optimizing the model using the Adam optimizer and monitoring metrics such as accuracy, precision, and recall.
Testing After training, the model's performance is evaluated on a separate test set to assess its generalization ability. Predictions are made on unseen images to determine the model's accuracy in classifying skin diseases.
Results and Interpretation The project concludes with an analysis of the model's performance metrics and visualizations of its predictions. This evaluation helps understand how well the model can identify different skin diseases based on input images.