[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-331454-105":59,"doc-detail-331454-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","optimising-ovarian-tumor-classification-using-a-novel-ct-sequence-selection-algorithm","Optimising ovarian tumor classification using a novel CT sequence selection algorithm","","Gynaecological cancers, particularly ovarian cancer, pose major public health challenges due to limited awareness, variable pathology, and restricted screening access, often causing late-stage diagnoses and poor patient outcomes. This study aims to improve accuracy in classifying ovarian tumours by separating malignant cases from early-stage cases using advanced deep learning. Three pre-trained models (Xception, ResNet50V2, and ResNet50V2FPN) are trained on selected CT images, while a novel CT sequence selection algorithm optimises CT image use. Results show the proposed algorithm outperforms the ResNet50V2FPN baseline with and without sequence selection, supporting earlier detection and management where resources are constrained.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/optimising-ovarian-tumor-classification-using-a-novel-ct-sequence-selection-algorithm/331454/",{"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/optimising-ovarian-tumor-classification-using-a-novel-ct-sequence-selection-algorithm/331454.png","ImageObject",300,407,{"name":92,"@type":93},"Sage","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address for ovarian cancer patients?","Question",{"text":112,"@type":113},"The study targets the challenge of classifying ovarian tumours accurately, especially distinguishing malignant cases from early-stage cases to improve outcomes after typically late-stage diagnoses.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which deep learning models are used in the proposed approach?",{"text":117,"@type":113},"The work uses three pre-trained models: Xception, ResNet50V2, and ResNet50V2FPN.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the novel CT Sequence Selection Algorithm affect performance?",{"text":121,"@type":113},"It optimises the use of CT images to improve classification, and comparative evaluation indicates the proposed algorithm outperforms the ResNet50V2FPN model both with and without CT sequence selection.","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},331454,1790413459,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"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},687197207057,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nOptimising ovarian tumor classification using a novel CT sequence selection algorithm  \nKV Bhuvaneshwari1, Husam Lahza2, B R Sreenivasa3􀀍, Hassan Fareed M Lahza4, Tawfeeq Shawly5 & B Poornima1  \nGynaecological cancers, especially ovarian cancer, remain a critical public health issue, particularly in regions like India, where there are challenges related to cancer awareness, variable pathology, and limited access to screening facilities. These challenges often lead to the diagnosis of cancer at advanced stages, resulting in poorer outcomes for patients. The goal of this study is to enhance the accuracy of classifying ovarian tumours, with a focus on distinguishing between malignant and early-stage cases, by applying advanced deep learning methods. In our approach, we utilized three pre-trained deep learning models—Xception, ResNet50V2, and ResNet50V2FPN—to classify ovarian tumors using publicly available Computed Tomography (CT) scan data. To further improve the model’s performance, we developed a novel CT Sequence Selection Algorithm, which optimises the use of CT images for a more precise classification of ovarian tumours. The models were trained and evaluated on selected TIFF images, comparing the performance of the ResNet50V2FPN model with and without the CT Sequence Selection Algorithm. Our experimental results show the Comparative evaluation against the ResNet50V2 FPN model, both with and without the CT Sequence Selection Algorithm, demonstrates the superiority of the proposed algorithm over existing state-of-the-art methods.  \nThis research presents a promising approach for improving the early detection and management of gynecological cancers, with potential benefits for patient outcomes, especially in areas with limited healthcare resources.  \nKeywords Cancer, Gynecological, Computed tomography (CT) sequence, ResNet50V2, CNN, Radiologist  \nGynecological cancers comprise a group of cancers that specifically target the female reproductive system, affecting various organs, including the ovaries, cervix, uterus, vagina, and vulva. These cancers can significantly impact women’s health and well-being. Diverse in their origins, symptoms, and treatment options, each type of cancer within this category possesses unique characteristics that may necessitate specific diagnostic approaches and treatment strategies. However, they all share the commonality of originating in women’s reproductive organs. Early detection and timely treatment are crucial in effectively managing gynecological cancers. Routine screenings, such as pap smears and pelvic exams, help to identify abnormalities in the cervix and uterus.  \nFurthermore, awareness of common symptoms and risk factors facilitates early identification of gynecological cancers. Treating these cancers typically involves a multidisciplinary approach encompassing surgery, radiation therapy, chemotherapy, targeted therapy, and hormonal therapy. Treatment choice depends on the specific type and stage of cancer and individual patient factors1. Gynecological cancers profoundly impact the lives of women and their loved ones. Medical professionals and researchers continually strive to enhance prevention strategies, early detection methods, and treatment options to alleviate the burden of these cancers and improve patient outcomes. Women must prioritize their reproductive health by maintaining regular check-ups, adopting healthy lifestyle habits, and promptly seeking medical attention if concerning symptoms arise. With advances in medical care and early detection, the prognosis for many gynecological cancers has improved, providing hope and support to those affected by these diseases.  \n1Department of Information Science & Engineering, Bapuji Institute of Engineering & Technology, Davanagere, Karnataka, India. 2Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz Univer","cbCaidZ0tVvbH4lW","https://ap.wps.com/l/cbCaidZ0tVvbH4lW","pdf",3748171,21,"English","# Overview\n## Public health challenge and motivation\n## Proposed deep learning approach\n## Experimental setup and results\n# Gynecological cancer background\n## Screening and early detection\n## Symptoms, risk factors, and treatment\n## Impact on patients and preventive guidance","[{\"question\":\"What problem does the study address for ovarian cancer patients?\",\"answer\":\"The study targets the challenge of classifying ovarian tumours accurately, especially distinguishing malignant cases from early-stage cases to improve outcomes after typically late-stage diagnoses.\"},{\"question\":\"Which deep learning models are used in the proposed approach?\",\"answer\":\"The work uses three pre-trained models: Xception, ResNet50V2, and ResNet50V2FPN.\"},{\"question\":\"How does the novel CT Sequence Selection Algorithm affect performance?\",\"answer\":\"It optimises the use of CT images to improve classification, and comparative evaluation indicates the proposed algorithm outperforms the ResNet50V2FPN model both with and without CT sequence selection.\"}]","Optimising ovarian tumor classification using a novel CT sequence selection algorithm | PDF",1789993306,53]