Prostate cancer detection using modified transformer with optimal feature selection from MRI images.

Prostate cancer is one of the most prevalent malignancies among men, and early detection through Magnetic Resonance Imaging (MRI) plays a crucial role in improving patient outcomes. However, accurate identification of cancerous regions remains challenging due to image noise, variations in tissue structures, and subtle differences between benign and malignant lesions. To address these challenges, a novel automated detection framework named Prostate Cancer Detection Neural Network (ProstateNet) that enhances the precision of prostate cancer detection is proposed. The proposed model begins with MRI image preprocessing using a Bilateral Filter, which effectively reduces noise while preserving edge details. Next, feature extraction is performed using a Residual Recurrent Model (RRM), which captures spatial dependencies and fine-grained image representations. To refine the extracted features, we employ the Iterative Secrecy Bird Optimization (ISBO) algorithm, ensuring optimal feature selection and reducing computational complexity. Finally, disease detection is carried out using a Modified Transformer model, which considers CapsuleNet instead of Feed Forward Network to accurately classify cancerous and non-cancerous regions. The integration of these advanced techniques significantly enhances the robustness and reliability of prostate cancer detection, offering a promising outcome.