## 1 Introduction  
Prostate cancer (PC) is the second most commonly diagnosed cancer and the fifth leading cause of mortality among men. More than 1.2 million new cases of prostate cancer were reported worldwide in 2018, representing approximately 7% of all cancers. Because prostate cancer is often asymptomatic at the early stage, with symptoms occurring primarily during later stages, it is crucial to have effective diagnostic tools that allow for early detection, vastly increasing the chances of successful patient treatment [1]. There are several methods of prostate cancer diagnosis: prostate-specific antigen (PSA) tests, computed tomography (CT), magnetic resonance imaging (MRI), PET scans, and transrectal ultrasounds (TRUSs) [2]. The most common diagnostic tool, the PSA test, is controversial due to issues of overdiagnosis and unnecessary prostate biopsies, which can lead to physical complications and significantly impact the quality of life of patients [3; 4; 5]. Moreover, PSA levels alone are not always reliable indicators of prostate cancer, which further emphasizes the need for more accurate, non-invasive diagnostic methods. Image-based examinations such as CT and MRI play an essential role in diagnosing prostate cancer, particularly when there are factors indicating a higher probability of cancer. CT scans offer the advantages of short evaluation times and wide availability due to their relatively low costs. However, CT has significant drawbacks, including radiation exposure, which limits its suitability for routine screening, and it has insufficient accuracy in soft tissue differentiation compared to MRI [6]. In contrast, MRI is generally preferred due to its superior soft tissue contrast, which allows for more precise tumor staging, better monitoring of treatment changes, and improved patient selection for biopsy. Furthermore, MRI has demonstrated higher diagnostic sensitivity than CT, particularly in detecting early prostate cancer [7]. The early detection of prostate cancer remains a significant challenge due to the limitations associated with current diagnostic methods. PSA testing, while widely used, lacks specificity, often resulting in overdiagnosis and unnecessary interventions. Similarly, CT scans, despite being cost-effective and readily accessible, have limitations in accurately distinguishing soft tissues, which compromises their effectiveness in early cancer detection. MRI, on the other hand, offers a more detailed view of prostate tissues, but is not always accessible or practical for all patients due to its higher cost and longer examination time. These challenges highlight the critical need for improved diagnostic techniques that combine accuracy, cost-efficiency, and accessibility. Addressing these issues can significantly improve patient outcomes by facilitating earlier and more accurate diagnoses, thus enhancing treatment effectiveness and reducing the physical and psychological burden on patients. In recent years, the integration of machine learning into medical imaging, particularly MRI and CT processing, has shown promising results in enhancing diagnostic accuracy and reducing variability between radiologists. Machine learning models can assist in highlighting suspicious areas within the imaging, leading to a more focused interpretation by radiologists during the scan analysis [8]. Fully automated segmentation of prostate MRI or CT scans can significantly accelerate the analysis process, reduce human error, and improve the overall efficiency of prostate cancer diagnosis. In addition, automated segmentation can aid radiologists by suggesting suspicious regions, thereby facilitating a more accurate and comprehensive evaluation. The present research aims to address these challenges by developing an efficient method for prostate segmentation in MRI using a hybrid deep learning approach.