## 1 Introduction  
Cancer is one of the most important diseases due to its mortality rates and impact on public health worldwide [1]. According to the World Health Organization (WHO), millions of people are diagnosed with cancer every year and this disease is considered to be one of the most common causes of death after cardiovascular diseases [2]. The most common types of cancer include lung, breast, colorectal, prostate, and stomach cancers [3]. Prostate cancer (PCa) is the second most common type of cancer in men and usually occurs at older ages. It is predicted that PCa cases will exceed 1.5 million annually [4]. Prostate cancer diagnosis rates are higher, especially in developed countries, because screening programsand early detection methods are more common. However, treatment success is often directly related to early diagnosis. Therefore, early diagnosis and management of prostate cancer have an important place in the fight against cancer. Although screening incentives, avoidance of carcinogens, balanced diet, and regular prostate-specific antigen (PSA) tests have been suggested to reduce PCa deaths, the contribution of these methods has been limited. Traditional diagnostic methods are generally invasive, which causes difficult processes for patients and heavy financial burdens on the healthcare system [5]. The prevalence of PCa, especially in countries with high elderly populations, increases the need for medical care and the workload on healthcare personnel. Therefore, the development of automated methods that provide less invasive and more accurate diagnoses from medical imaging data has become an important research area in PCa diagnosis [6]. In recent years, artificial intelligence techniques such as deep learning (DL) and transfer learning (TL) have made significant advancements, particularly in the field of image classification [7]. DL models, such as convolutional neural networks (CNNs), have achieved high success rates in image processing and classification tasks. TL, on the other hand, delivers faster and more effective results by fine-tuning previously trained models for new tasks [8]. In this study, a CNN model was developed to classify prostate magnetic resonance imaging (MRI) data to achieve effective prostate cancer detection (PCaD). This study also aims to enhance the accuracy of this model by employing the TL technique. The use of artificial intelligence in the healthcare sector has emerged as a promising approach for PCaD [9]. In addition to various DL and TL models, machine learning (ML) models also strive to improve diagnostic accuracy while reducing the workload of medical personnel [10]. Positron emission tomography (PET) and computed tomography (CT), like MRI, are fundamental tools used to image tissues and organs in PCaD. However, these images often contain noise that must be eliminated before diagnosis [11]. In the study by [PERSON] et al., PSMA PET/CT achieved an overall diagnostic accuracy rate of 92.3% in detecting nodal metastases in clinically significant PCa (especially ISUP Grade Group 2), with 100% accuracy in Grade Group 2 patients and 63.6% in Grade Group 5 patients; furthermore, negative PSMA PET/CT results did not exclude the need for extended pelvic lymph node dissection (ePLND) in high-risk patients or in the presence of ductal adenocarcinoma [12]. [PERSON] et al. have determined that the Gleason score determined by biopsy, which is important in determining the treatment method in PCa, was 82.6% consistent with the Gleason score after radical prostatectomy, but in 17.4% of patients, the biopsy Gleason score underestimated the true aggressiveness of the tumor [13]. The study revealed that the Gleason score agreement increased when the ratio between the total volume of the biopsy cores in the tumor area and the tumor volume after radical prostatectomy was >0.05 and that certain threshold values related to the tumor volume during biopsy and the total biopsy volume were critical for accurate Gleason score estimation. In a review study, [PERSON] et al.