The analysis aims to detect and classify liver diseases using medical images of patients. The project utilizes the architecture of convolutional neural networks, which is particularly effective in image analysis.

Upon opening the file "liver-disease-analysis.ipynb," you will find a series of code cells and explanatory text. The notebook begins with the importation of necessary libraries, followed by data preparation.

Next, the dataset is preprocessed and divided into training and testing sets. The project utilizes the Keras library, which is a high-level interface for building and training neural networks.

The architecture of the convolutional neural network is defined and compiled. The model is trained using the training data and then evaluated using the testing data to assess its accuracy in detecting liver diseases.

The project also includes visualizations of the results, such as accuracy and loss graphs during training, as well as a confusion matrix to evaluate the model's performance across different classes of liver diseases.

In addition to the code, the notebook also contains detailed explanations of the analysis process, the logic behind each step, and the parameters used.

Attention!

This is an unvalidated project for AI study purposes. Do not consider the results presented. Please consult a specialized doctor.