## 2 Related Works  
In recent years, many researchers have addressed the problem of classifying PCa MRI images using ML, DL, and TL techniques. Although publicly available datasets for PCa MRI image classification are available, most of them have limited or incomplete access. Various models have been developed for PCaD, and different techniques have been applied to improve their performance. [PERSON] et al. proposed a method for PCa classification by pre-training InceptionV3 and Vgg16 models on ImageNet and fine-tuning them with multi-parameter MRI data [19]. [PERSON] et al. used residual networks that can learn low-level and high-level features to detect PCa and demonstrated the potential of these networks in detecting complex disease symptoms [20].  
[PERSON] et al. proposed a novel DL technique combining CNN and TL for PCaD in MRI images, achieving 87% accuracy, 85% specificity, and 89% sensitivity [21]. [PERSON] et al. investigated DL techniques in histopathology images for PCa diagnosis and Gleason grading, demonstrating the potential for accurate diagnosis and classification [22]. [PERSON] et al. systematically evaluated DL techniques in the digital pathology of PCa, summarizing the current status, innovations, and challenges, and achieved 99% AUC performance [23].  
[PERSON] et al. used deep CNN and 3D sliding window techniques to segment prostate lesions in T2W MRI images, achieving 99% AUC, 89% accuracy, and 92% recall performance [24]. [PERSON] et al. evaluated the performance of supervised classifiers for PCaD with T2W MRI in a multicenter approach, aiming to improve the treatment process through early and accurate diagnosis. QDA classifier showed the highest performance with AUC values of 0.735, 0.683, and 0.768, respectively, for detecting PCa spread in three centers [25]. [PERSON] and [PERSON] proposed a method for PCa grading by combining automatic and manually generated features; this method achieved a kappa score of 0.2326 and PPV of 80.26% using autoencoders to extract features and softmax for classification [26]. [PERSON] et al. developed a patch-based DCNN model to distinguish cancerous and non-cancer patients with MRI data based on regions manually marked by radiologists; the model performed with an AUC of 0.944, sensitivity of 87%, and specificity of 90.6% [27].  
[PERSON] et al. used Faster R-CNN and PI-RADSAI methods in PCaD, achieving improvement in accuracy and speed with Adam and SGD dual-optimizers and human-loop DL analysis [28]. [PERSON] et al. achieved 92.38% accuracy on histopathological images with a model composed of UNet, Xception, and EfficientnetB7 [29]. [PERSON] et al. developed a CNN method to predict the origin of bone metastatic cancer, which surpassed existing methods with 95.2% accuracy [30]. [PERSON] et al. achieved 95.24% accuracy and high sensitivity by improving the ResNet50-based PCaDM model with Faster R-CNN and dual optimizers [31]. [PERSON] et al. proposed an automatic cancer detection system that achieved 97% accuracy on prostate biopsy images [32].  
Recent studies have aimed to improve the accuracy and speed of DL, TL, and ViT methods for MRI-based PCaD. However, there are limitations such as dependency on large labeled datasets, interpretability difficulties, and risks of overfitting. To overcome these problems, careful data processing methods such as hybrid models and pyramid network learning strategies adapted to small datasets are used in this study.