[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-368241-en":3,"doc-seo-368241-105":31,"detail-sidebar-cat-0-en-105":98},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},368241,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",7,"Healthcare","Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging","Oral carcinoma presents a major global public health challenge, with late diagnosis, high incidence, and inadequate treatment strategies limiting effective outcomes. This study develops the DSLVI-OCLSC deep structured learning framework to improve early detection and support accurate classification and recognition using medical imaging. The method applies Wiener filtering to reduce noise, ShuffleNetV2 to extract higher-level deep features, and a convolutional bidirectional long short-term memory network with multi-head attention for oral carcinoma identification. UNet3+ segments abnormal regions, while the sine cosine algorithm tunes hyperparameters to optimize deep learning performance. Experiments on an OC image dataset report superior accuracy of 98.47% compared with recent approaches.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nDeep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging  \nAhmad A. Alzahrani1, Jamal Alsamri2, Mashael Maashi3, Noha Negm4, SomiaA. Asklany5􀀍, AbdulwhabAlkharashi6, Hassan Alkhiri7 & Marwa Obayya1  \nOral carcinoma (OC) is a toxic illness among the most general malignant cancers globally, and it has developed a gradually significant public health concern in emerging and low-to-middle-income states. Late diagnosis, high incidence, and inadequate treatment strategies remain substantial challenges. Analysis at an initial phase is significant for good treatment, prediction, and existence. Despite the current growth in the perception of molecular devices, late analysis and methods near precision medicine for OC patients remain a challenge. A machine learning (ML) model was employed to improve early detection in medicine, aiming to reduce cancer-specific mortality and disease progression.  \nRecent advancements in this approach have significantly enhanced the extraction and diagnosis of critical information from medical images. This paper presents a Deep Structured Learning with Vision Intelligence for Oral Carcinoma Lesion Segmentation and Classification (DSLVI-OCLSC) model for medical imaging. Using medical imaging, the DSLVI-OCLSC model aims to enhance OC’s classification and recognition outcomes. To accomplish this, the DSLVI-OCLSC model utilizes wiener filtering (WF) as a pre-processing technique to eliminate the noise. In addition, the ShuffleNetV2 method is used for the group of higher-level deep features from an input image. The convolutional bidirectional long short-term memory network with a multi-head attention mechanism (MA-CNN-BiLSTM) approach is utilized for oral carcinoma recognition and identification. Moreover, the Unet3 + is employed to segment abnormal regions from the classified images. Finally, the sine cosine algorithm (SCA) approach is utilized to hyperparameter-tune the DL model. A wide range of simulations is implemented to ensure the enhanced performance of the DSLVI-OCLSC method under theOC images dataset. The experimental analysis of the DSLVI-OCLSC method portrayed a superior accuracy value of 98.47% over recent approaches.  \nKeywords Deep learning, Oral carcinoma, Weiner filtering, Sine cosine algorithm, Medical imaging  \nCancer is a leading public health difficulty and the 2nd common reason for death in advanced countries. OC is among the ten typical cancers; above 90% are squamous cell carcinomas1. Regardless of therapeutic and diagnostic growth in OC patients, morbidity and mortality rates have stayed higher with no development in the past 50 years, mainly owing to the last phase of diagnosis when metastatic cancer appeared2. Regularly, oral squamous cell carcinoma (OSCC) occurs from previous lesions of oral mucosa with an improved threat for malignant  \n1Department of Computer Science and Artificial Intelligence, College of Computing, Umm-AlQura University, Mecca, Saudi Arabia. 2Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. 3Department of Software Engineering, College of Computer and Information Sciences, King Saud University, PO Box 103786, 11543 Riyadh, Saudi Arabia. 4Department of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia. 5Department of Computer Science and Information Technology, Faculty of Sciences and Arts, Turaif, Northern Border University, 91431 Arar, Saudi Arabia. 6Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia. 7Department of Computer Science, Faculty of Computing and Information Technology, AlBaha University, Al-Baha, Saudi Arabia. 􀀍 email: [somia.asklany@nbu.edu.sa](somia.asklany@nbu.edu.sa)  \n[www. nature.com/scientificreports/](www. na","cbCaigmyNEOxumWF","https://ap.wps.com/l/cbCaigmyNEOxumWF","pdf",4833754,3,1,22,"English","en",105,"# Introduction\n## Oral carcinoma challenges and diagnostic needs\n# Proposed DSLVI-OCLSC method\n## Pre-processing with Wiener filtering\n## Feature extraction with ShuffleNetV2\n## MA-CNN-BiLSTM recognition\n## UNet3+ segmentation\n## Hyperparameter tuning with SCA\n# Experiments and results\n## Performance evaluation on OC image dataset\n## Reported accuracy comparison","[{\"question\":\"What is the main goal of the DSLVI-OCLSC model?\",\"answer\":\"It aims to enhance oral carcinoma classification and recognition outcomes using medical imaging, supporting more accurate early analysis.\"},{\"question\":\"Which techniques are used for noise reduction and feature extraction?\",\"answer\":\"Wiener filtering is used for pre-processing to remove noise, and ShuffleNetV2 extracts higher-level deep features from the input image.\"},{\"question\":\"How are lesions segmented and how are hyperparameters optimized?\",\"answer\":\"UNet3+ segments abnormal regions from the classified images, while the sine cosine algorithm (SCA) performs hyperparameter tuning for the deep learning model.\"}]","Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging | 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