Chapter 1: Introduction to CNNs and the SCZ Dataset - This chapter introduces the concept of CNNs and the SCZ dataset, which is a collection of MRI images of patients with schizophrenia and healthy controls. Chapter 2: Preprocessing the SCZ Dataset - This chapter discusses the importance of preprocessing data before training a CNN model and provides code examples of how to preprocess the SCZ dataset using Python. Chapter 3: Building a CNN Model from Scratch - This chapter demonstrates how to build a CNN model from scratch using the Keras API in Python and trains the model on the preprocessed SCZ dataset. Chapter 4: Evaluating the CNN Model - This chapter discusses how to evaluate the performance of a CNN model using various metrics such as accuracy, precision, and recall, and provides code examples for implementing these metrics in Python. Chapter 5: Exploring CNN Architectures - This chapter explores different CNN architectures such as LeNet, AlexNet, VGG, and ResNet, and discusses their pros and cons. Chapter 6: Data Augmentation - This chapter discusses the concept of data augmentation, which is a technique used to artificially increase the size of a dataset by creating modified versions of the original data. The chapter also provides code examples of how to implement data augmentation in Python. Chapter 7: Transfer Learning - This chapter introduces the concept of transfer learning, which involves using a pre-trained model to improve the performance of a new model on a different but related dataset. The chapter provides code examples of how to fine-tune a pre-trained VGG16 model on the SCZ dataset using the Keras API in Python. Chapter 8: Fine-Tuning a Pretrained CNN on the SCZ Dataset - This chapter continues the discussion of fine-tuning a pre-trained VGG16 model on the SCZ dataset using the Keras API in Python, and provides code examples of how to compile and train the model, as well as evaluate its performance on the preprocessed testing data. Overall, this book provides a comprehensive overview of CNNs and their application to the SCZ dataset, covering topics such as preprocessing, model building, evaluation, exploring different architectures, data augmentation, and transfer learning. The code examples provided in Python using the Keras API make it a practical resource for those interested in applying CNNs to neuroimaging datasets.