[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-detail-346143-en":59,"doc-seo-346143-105":80},{"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":5,"data":60},{"doc_id":61,"user_id":62,"nickname":63,"user_avatar":64,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":66,"doc_content":67,"file_id":68,"file_url":69,"file_type":70,"file_size":71,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":72,"language":73,"language_code":74,"site_id":75,"html_lang":74,"table_of_contents":76,"faqs":77,"seo_title":78,"seo_description":66,"update_tm":79,"read_time":41},346143,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","Bone Cancer Cell Prediction Using an Enhanced Deep Learning Algorithm with an Optimization Technique","Bone cancer cell detection requires reliable automated systems to support early diagnosis and reduce the time, expertise, and error-proneness of manual image assessment. The study proposes an ML-driven pipeline combining deep learning with optimization, using feature extraction followed by Cuckoo Search optimization. A CS-MHC ResNet model improves image classification and outperforms VGG-16, Inception, and Xception in accuracy, sensitivity, precision, and F-measure, indicating clinical promise. Future work targets larger multi-center datasets.","RESEARCH ARTICLE  \n\n| Editorial Proc | ess: Submission:02/26/2025 Acceptance:02/27/2026 Published:03/06/2026 |\n| --- | --- |\n\nBone Cancer Cell Prediction Using an Enhanced Deep Learning Algorithm with an Optimization Technique  \nMohanthi Kakarla1 *, K Padma Raju2  \nAbstract  \nObjective: Bone cancer is a very serious disorder that can be fatal for many people. A reliable detection and classification system is required for early-stage bone cancer diagnosis. Traditional manual approaches are time-consuming and need specific skills, thus the creation of an automated system for detecting and classifying malignant and healthy bone tissue is critical. Cancerous bone tissue often has a different texture than healthy tissue in the affected location. Despite initially using Support Vector Machine and Edge Detection methods to improve outcomes, we only reached 0.92% accuracy. As a result, moving to deep learning is important for improved performance. Our strategy will begin with feature extraction, followed by the usage of the Cuckoo Search optimization algorithm. Methods: The methodology emphasizes rigorous data preprocessing, model evaluation using standard metrics, and clinical integration for real-world application. It aims to develop a machine learning (ML)-driven tool for bone cancer detection by utilizing a combination of deep learning (DL) models and optimization methods. It includes enhancing detection accuracy by integrating Cuckoo Search Modified Hill Climbing (CS-MHC) optimization with ResNet for improved image classification, optimizing model performance through CSO for better feature selection and faster convergence, comparing the CS-MHC ResNet model with traditional models like VGG-16, Inception, and Xceptionto improve accuracy, precision, and recall, creating a clinically applicable model for early bone cancer diagnosis and contributing to medical image analysis by combining hybrid optimization and deep learning techniques. The CNN will serve as the primary model for image classification, while Cuckoo Search Optimization will enhance feature selection and hyperparameter tuning. Results: CS-MHCResNet demonstrated superior performance over other models in classification accuracy (above 90%), sensitivity (around 85%), precision (above 88%), and F-measure (approximately 86%) . Other models (VGG-16, Xception, Inception) showed lower performance, indicating that the integration of CSO with ResNet enhances feature selection and improves the method’s ability to classify bone cancer more effectively. These outcomes indicate that the proposed CS-MHC ResNet method offers significant improvements in the automated detection of bone cancer, supporting its potential for clinical use in diagnostic systems. Conclusion: The CS-MHC ResNet model combines Cuckoo Search Optimization (CSO) with ResNet for automated bone cancer detection. The model outperformed traditional deep learning architectures like VGG-16, Xception, and Inception in accuracy, sensitivity, precision, and F-measure. Key findings include enhanced model performance, improved feature selection via CSO, and faster convergence. The CS-MHC ResNet model shows promise for clinical applications, offering a more efficient and reliable tool for bone cancer detection. Future research will concentrate on larger multi-center datasets and simpler designs to improve resilience and applicability.  \nKeywords: Machine learning (ML)-Deep learning (DL)-Support vector machines (SVM)-Ant Colony Optimization  \nAsian Pac J Cancer Prev, 27 (3), 839-850  \nIntroduction  \nAccording to the American Cancer Society, there will be about 3,970 new cases of primary bone and joint cancer in 2024 (2,270 in men and 1,700 in women), along with about 2,050 deaths (1,100 in men and 950 in women) . These numbers include both adults and children. Less than 1% of all cancers are primary bone cancers, which start in the bones. Primary bone cancer is far less common in  \nadults than bone metastasis cancer, which spread","cbCailoAml7oyvVS","https://ap.wps.com/l/cbCailoAml7oyvVS","pdf",1323787,12,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Background on bone cancer and early detection\n## Challenges with MRI/X-rays and manual analysis\n## Rationale for ML and deep learning","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses the need for reliable automated detection and classification of bone cancer cells for early-stage diagnosis, where manual methods are slow and error-prone.\"},{\"question\":\"What optimization and model combination is proposed?\",\"answer\":\"It proposes feature extraction plus Cuckoo Search optimization, integrating Cuckoo Search Modified Hill Climbing (CS-MHC) with ResNet to improve image classification.\"},{\"question\":\"How does the proposed method perform compared with traditional models?\",\"answer\":\"CS-MHC ResNet achieves higher classification performance (accuracy above 90%, sensitivity around 85%, precision above 88%, and F-measure around 86%) compared with VGG-16, Inception, and Xception.\"}]","Bone Cancer Cell Prediction Using an Enhanced Deep Learning Algorithm with an Optimization Technique | PDF",1790060211,{"code":4,"msg":81,"data":82},"ok",{"site_id":75,"language":74,"slug":83,"title":65,"keywords":84,"description":66,"schema_data":85,"social_meta":139,"head_meta":141,"extra_data":143,"updated_unix":144},"bone-cancer-cell-prediction-using-an-enhanced-deep-learning-algorithm-with-an-optimization-technique","",{"@graph":86,"@context":138},[87,101,121],{"@type":88,"itemListElement":89},"BreadcrumbList",[90,94,96,99],{"item":91,"name":92,"@type":93,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":95,"name":9,"@type":93,"position":14},"https://docshare.wps.com/document/",{"item":97,"name":40,"@type":93,"position":98},"https://docshare.wps.com/document/research-report/",3,{"item":100,"name":65,"@type":93,"position":19},"https://docshare.wps.com/document/bone-cancer-cell-prediction-using-an-enhanced-deep-learning-algorithm-with-an-optimization-technique/346143/",{"url":100,"name":65,"@type":102,"image":103,"author":108,"headline":65,"publisher":110,"fileFormat":113,"inLanguage":74,"description":66,"dateModified":114,"datePublished":115,"encodingFormat":113,"isAccessibleForFree":116,"interactionStatistic":117},"DigitalDocument",{"url":104,"@type":105,"width":106,"height":107},"https://docshare.wps.com/thumbnails/bone-cancer-cell-prediction-using-an-enhanced-deep-learning-algorithm-with-an-optimization-technique/346143.png","ImageObject",300,407,{"name":63,"@type":109},"Person",{"url":91,"name":111,"@type":112},"DocShare","Organization","application/pdf","2026-09-23","2026-09-22",true,{"@type":118,"interactionType":119,"userInteractionCount":14},"InteractionCounter",{"@type":120},"ViewAction",{"@type":122,"mainEntity":123},"FAQPage",[124,130,134],{"name":125,"@type":126,"acceptedAnswer":127},"What problem does the document address?","Question",{"text":128,"@type":129},"It addresses the need for reliable automated detection and classification of bone cancer cells for early-stage diagnosis, where manual methods are slow and error-prone.","Answer",{"name":131,"@type":126,"acceptedAnswer":132},"What optimization and model combination is proposed?",{"text":133,"@type":129},"It proposes feature extraction plus Cuckoo Search optimization, integrating Cuckoo Search Modified Hill Climbing (CS-MHC) with ResNet to improve image classification.",{"name":135,"@type":126,"acceptedAnswer":136},"How does the proposed method perform compared with traditional models?",{"text":137,"@type":129},"CS-MHC ResNet achieves higher classification performance (accuracy above 90%, sensitivity around 85%, precision above 88%, and F-measure around 86%) compared with VGG-16, Inception, and Xception.","https://schema.org",{"og:url":100,"og:type":140,"og:title":65,"og:site_name":111,"og:description":66},"article",{"robots":142,"canonical":100},"index,follow",{"doc_id":61,"site_id":75},1790202830]