## 1 Introduction
summarize the diagnostic methods of PCa, genetic mutations that play a role in the onset and progression of the disease, current treatment options, and research on alternative approaches such as conventional medicine, nanotechnology, and gene therapy, highlighting the impact of combined treatment strategies despite the disease still being incurable [14]. Transferring knowledge from different datasets to the target dataset through TL can significantly enhance the accuracy of PCa diagnosis models [15]. In this context, the fine-tuned enhanced pyramid network (FT-EPN) model proposed in our study was tested on a dataset containing benign and malignant PCa MRI images. To evaluate the performance of the proposed model for PCaD, tests were conducted on a publicly available dataset, and the results were analyzed. The developed model aims to enhance the PCaD process and add value to the healthcare system by contributing to accurate and rapid diagnosis. This study introduces a new approach to PCaD, addressingspecific limitations identified in previous research. In some prior experiments, the entire benchmark dataset was not fully utilized, or only a single split validation was typically performed. Additionally, the accuracy of many existing models remains below 90% and requires improvement. Furthermore, some studies revealed significant discrepancies between precision and recall, leading to classification bias [16; 17; 18]. To address these limitations, a robust CNN with a fine-tuned structure and high accuracy in classifying PCa MRI images was developed in this study. Regularization techniques and hyperparameter tuning were applied to prevent overfitting and improve the model's generalization ability. A deep CNN architecture was designed to capture the complex features of PCa MRI images, achieving a classification accuracy of 96.77%. The model's effectiveness was further evaluated using common TL models and popular architectures such as ViT and MaxViT-v2. In this study, we propose FT-EPN, a fine-tuned, enhanced pyramid-type deep network model that facilitates more effective learning of complex features. This is achieved by gradually adjusting the number of neurons in the layers, enabling accurate classification of PCa MRI images. The main contributions of the paper are summarized as follows:  
* A powerful and flexible FT-EPN model was developed to classify PCa images. Unlike the basic model, this model can be fine-tuned in terms of the number of layers, neuron structure, and activation functions. Its multi-layered structure provides high accuracy by extracting both local and abstract features. * To improve model performance, architectural depth was enhanced using regularization techniques such as L2 regularization, dropout layers, Tanh activation, early stopping, and an appropriate learning rate. The effects of hyperparameters were tested through large-scale studies to determine the most suitable configuration, preventing overfitting and improving generalization ability. * A deep, pyramid-type CNN architecture was designed to better capture the complex features of prostate images. The information extraction capacity of the model was increased by adding convolutional layers before the MaxPooling layers, resulting in a classification accuracy of 96.77%. * The effective performance of the developed FT-EPN model was evaluated using various metrics. Classification was performed with Vgg16, Vgg19, ResNet50, InceptionV3, DenseNet121, and Xception architectures pre-trained on ImageNet, as well as popular TL models such as ViT and MaxViT-v2.