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The proposed ProstateNet framework applies MRI preprocessing with a Bilateral Filter, extracts features via a Residual Recurrent Model (RRM), and performs iterative secrecy bird optimization (ISBO) to select discriminative features while lowering computational complexity. Classification is achieved with a Modified Transformer using CapsuleNet instead of a Feed Forward Network.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/prostate-cancer-detection-using-modified-transformer-with-optimal-feature-selection-from-mri-images-read-online/345966/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/prostate-cancer-detection-using-modified-transformer-with-optimal-feature-selection-from-mri-images-read-online/345966.png","ImageObject",300,407,{"name":92,"@type":93},"Evangeline","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-24","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the proposed ProstateNet framework address?","Question",{"text":112,"@type":113},"It tackles the challenge of accurately detecting prostate cancerous regions in MRI images despite noise, tissue-structure variability, and small differences between benign and malignant lesions.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does ProstateNet improve MRI image quality before feature extraction?",{"text":117,"@type":113},"It uses a Bilateral Filter for preprocessing to reduce noise while preserving edge details relevant for distinguishing lesions.",{"name":119,"@type":110,"acceptedAnswer":120},"What role does ISBO play in the method?",{"text":121,"@type":113},"ISBO (Iterative Secrecy Bird Optimization) refines extracted features by performing optimal feature selection and reducing computational complexity before classification.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},345966,1790216678,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},13056703019662,"https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nProstate cancer detection using modified transformer with optimal feature selection from MRI images  \nM. R. Prathap1􀀍, K. S. Vairavel1, C. Kumar3 & Abdullah Alwabli2  \nProstate 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.  \nKeywords Secrecy Bird Optimization, Bilateral Filter, Residual Network, Feature Selection, Prostate Cancer Detection  \nProstate cancer is a significant health concern, being the most common malignancy among men in several countries, including Australia, where approximately 25,000 men are diagnosed annually, leading to over 3,500 deaths each year1. The exact causes of prostate cancer remain unclear; however, risk factors such as age, family history, and diets high in saturated fats have been identified2. Early detection of prostate cancer is crucial, as it significantly improves survival rates and allows for a broader range of treatment options. Early-stage prostate cancer often presents with minimal or no symptoms, making proactive screening vital3,4. Digital rectal examination (DRE) and the prostate-specific antigen (PSA) blood test are examples of conventional detection techniques. Increased PSA levels may be a sign of cancer. The PSA test quantifies the amount of PSA in the blood. However, because elevated PSA levels can also be caused by benign illnesses such prostatitis or benign prostatic hyperplasia, PSA testing has been criticized for its lack of specificity, which results in over-diagnosis and overtreatment5. The DRE has limits in terms of sensitivity and specificity, but it uses a physical examination of the prostate gland to identify abnormalities.  \nBased on pathological grading, PSA level, and tumor stage, prostate cancer is generally divided into clinically significant and clinically non-significant diseases in clinical practice. According to the TNM (Tumor, Node, Metastasis) staging system, clinically relevant prostate cancer usually refers to aggressive tumors that need tobe treated definitively and are linked to Gleason Grade Group ≥ 2, PSA levels typically greater than 10 ng/mL, or clinical stage ≥ T2b. These tumors are linked to an increased risk of progression, extracapsular extension, or metastasis and frequently match PI-RADS 4–5 lesions on multiparametric MRI. On the other hand, Gleason Grade Group 1, PSA values usually less than 10 ng/mL, and clinical stage T1–T2a are widely used to describe indolent or low-risk prostate cancer. Active surveillance is often used to control these tumors instead of prompt intervention. In order to prevent overtreatment and ensure prompt management of aggre","cbCailDIDAgHJiFW","https://ap.wps.com/l/cbCailDIDAgHJiFW","pdf",4617075,22,"English","# Prostate cancer detection background\n## Challenges in region identification from MRI\n# Proposed framework: ProstateNet\n## MRI preprocessing with Bilateral Filter\n## Feature extraction with Residual Recurrent Model (RRM)\n## Feature refinement with ISBO\n## Modified Transformer with CapsuleNet classification\n# Clinical context and risk stratification\n## Clinically significant vs non-significant disease\n## PSA, DRE, and TNM/Gleason/PI-RADS criteria\n# Related methods and motivation\n## Traditional ML approaches\n## Deep learning approaches and limitations","[{\"question\":\"What problem does the proposed ProstateNet framework address?\",\"answer\":\"It tackles the challenge of accurately detecting prostate cancerous regions in MRI images despite noise, tissue-structure variability, and small differences between benign and malignant lesions.\"},{\"question\":\"How does ProstateNet improve MRI image quality before feature extraction?\",\"answer\":\"It uses a Bilateral Filter for preprocessing to reduce noise while preserving edge details relevant for distinguishing lesions.\"},{\"question\":\"What role does ISBO play in the method?\",\"answer\":\"ISBO (Iterative Secrecy Bird Optimization) refines extracted features by performing optimal feature selection and reducing computational complexity before classification.\"}]","Prostate cancer detection using modified transformer with optimal feature selection from MRI images - read online | PDF",1790059561,55]