## 5 Conclusions  
Early diagnosis of PCa is a critical factor in the course of the disease and the treatment process. Computerized diagnostic systems offer significant advantages over traditional methods by detecting small changes or details that the human eye cannot notice. In this study, it was demonstrated that the proposed FT-EPN model can assist doctors in detecting PCa cancerous tissues by providing fast and effective analysis. Thus, it aims to reduce the workload of doctors as an alternative to time-consuming and error-prone applications in traditional manual diagnosis processes. In this study, the FT-EPN model was developed and compared with different DL architectures to achieve high accuracy in classifying PCa MRI images. The FT-EPN model we developed achieved an accuracy rate of 96.77%, thanks to an effective combination of techniques such as the optimized pyramid structure, Tanh activation function, L2 regularization, and early stopping. This accuracy rate represents a 7% increase compared to the accuracy of the initially created Sequential-based model. With the added convolutional layers and appropriate regularization coefficients, the risk of overfitting was reduced, enabling the model to learn complex features more effectively. As a result of comparisons with TL models and new-generation architectures such as the popular ViT and MaxViT-v2, it was observed that our fine-tuned enhanced model provided remarkable success, especially on small two-class datasets. While the Vgg16 and Vgg19 models with TL achieved accuracy rates of 91.13% and 92.74%, respectively, the ViT model achieved 93.55% and the MaxViT-v2 hybrid model achieved 95.16%. However, our proposed FT-EPN model for PCaD offers advantages in terms of both high accuracy and model simplicity when compared to TL and new-generation transformer models. Finally, it has been shown that the fine-tuned model developed in this study is optimized according to the size and classification structure of the dataset and provides an effective solution in PCa classification by exhibiting high performance compared to the accuracy of other models. The pyramid structure of the model and the integrated optimization techniques provide strong classification performance on small datasets, making it an important reference for future research. In the future, the proposed model is expected to be further developed by training it with comprehensive datasets and to work effectively in real-world conditions.  
Conceptualization, E.O. and M.S.; methodology, E.O. and M.S.; implementation, M.S.; supervision, E.O.; data curation, M.S. All authors declare equal and joint responsibility for the study. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Scientific Research Projects Management Unit of Firat University under project MF.24.103.  
Not applicable.  
All relevant data are fully available within the manuscript without restriction. The Kaggle data that support this study's conclusions are publicly available online: [[https://www.kaggle.com/code/waichunchim/prostate-cancer-inception-v3/input](https://www.kaggle.com/code/waichunchim/prostate-cancer-inception-v3/input)]([https://www.kaggle.com/code/waichunchim/prostate-cancer-inception-v3/input](https://www.kaggle.com/code/waichunchim/prostate-cancer-inception-v3/input)). (accessed on 29 November 2024).  
The authors declare no conflicts of interest.