[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-368884-105":59,"doc-detail-368884-en":131},{"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":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","comprehensive-brain-tumour-concealment-utilizing-peak-valley-filtering-and-deeplab-segmentation","Comprehensive brain tumour concealment utilizing peak valley filtering and deeplab segmentation","","Brain tumour identification, segmentation, and boundary delineation from MRI images are essential for medical image analysis, yet accurate performance often comes with high training and testing computational costs. This study evaluates deep learning architectures—XceptionNet and MobileNet for classification, and DeepLab for segmentation—trained on the BRATS 2018 dataset. DeepLab achieves the strongest segmentation results, while XceptionNet and MobileNet deliver high classification accuracy. Results indicate precise segmentation and robust diagnostic potential, supporting more effective brain tumour analysis and treatment workflows.",{"@graph":69,"@context":123},[70,84,106],{"@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/comprehensive-brain-tumour-concealment-utilizing-peak-valley-filtering-and-deeplab-segmentation/368884/",{"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/comprehensive-brain-tumour-concealment-utilizing-peak-valley-filtering-and-deeplab-segmentation/368884.png","ImageObject",300,407,{"name":92,"@type":93},"Mary Man","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Which deep learning models are evaluated for classification and segmentation?","Question",{"text":113,"@type":114},"XceptionNet and MobileNet are evaluated for classification, while DeepLab is used for segmentation of the cancerous region.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What dataset is used to train and evaluate the models?",{"text":118,"@type":114},"All architectures are trained and evaluated using the BRATS 2018 dataset.",{"name":120,"@type":111,"acceptedAnswer":121},"How is segmentation performance assessed, and what is the reported best result?",{"text":122,"@type":114},"Segmentation is assessed with Pearson Correlation Coefficient values, with DeepLab producing the best segmentation result (reported as 0.50/0.51 in the text) and additional agreement metrics.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},368884,1790378292,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146},7421720224475,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nComprehensive brain tumour concealment utilizing peak valley filtering and deeplab segmentation  \nB. P Pradeep Kumar1, E Naresh2􀀍, C. K Raghavendra2􀀍 & Raguru Jayakrishna3􀀍  \nBrain tumour identification, segmentation cataloguing from MRI images is most thought-provoking and is a very much essential for many medical image analysis applications. Every brain imaging modality provides information about various parts of the tumor. In current years deep learning systems have shown auspicious outcomes in medical image investigation tasks. Despite several recent works achieved a significant result on brain tumour segmentation and classification, they come with an improved performance atthe expense of increased computational complexity to train and test  \nthe system. This exploration paper investigates the efficacy of popular deep learning architectures namely Xception Net, MobileNet for classification and DeepLab for segmentation of the cancerous region of brain tumor. Each architecture is trained using a BRATS 2018 dataset and evaluated for its performance in accurately classifying tumor presence and delineating tumor boundaries. The DeepLab models accomplished a best segmentation result with Pearson Correlation Coefficient values 0.50 respectively and the deep learning models Xception Net and MobileNet achieved an accuracy of 0.8921 and 0.9176respectively. The experimental results show that these architectures achieve high accuracy and precise segmentation. The findings ofthis study contribute to advancing the field of medical image analysis and hold implications for improving the analysis and dealing of brain tumors.  \nKeywords Brain tumour analysis, Xception Net, Mobile Net, DeepLab segmentation, MRI images  \nThe growth of abnormal tissues is the major cause for brain Tumours. It affects or in some cases it halts the regular functions of the brain1. Malignant and benign are the two important classes of brain tumour2. The later class is curable and occur repeatedly and the former one is not curable and affects other regions of the brain rapidly3. The extensively applied modalities for detecting these tumours are CT, PET, and MRI4,5. Also the Medical Image Interpretation is popularly utilized for Research and Treatment in Medical community. This has several examples from image analysis6, medical bots7 to computer assisted diagnosis7. Among all the medical problems, brain tumour is the consistently explored and very fascinating medical challenge among all other challenges reported in the literature. The accurate early detection of these tumour very much essential to provide the quick treatment to the affected patients. The research community has proposed several models with respect to the brain tumour detection and classification in the literature. Despite huge effort from the research community the precise detection, dissection and cataloguing of brain tumour remain as a challenge. This may be due to Brain Tumor (BT) is a great healthcare challenge, with its diagnosis and treatment heavily reliant on accurate image analysis. Manual analysis of MRI scans is tedious, and practitioners are in need for automated methods to assist clinicians in interpreting medical images. Recently Deep Learning (DL) has sought huge attention for medical image analysis offering promising results for automating problems like Tumor Segmentation and Classification (TSC) . But convolutional neural networks (CNNs) are proficient of learning multifaceted patterns from large scale data surpassing baseline systems in several medical imaging tasks. During past, several models have been contributed by researchers in the literature as a solution for brain tumour related problems8,9.  \nThe brain tumour diagnosis consists of pre-processing, parameterization of images, segmentation, and classification steps.  \nPre-processing phase is focused with the noise reduction, enhancement and skull–","cbCaip27oV0fhwjK","https://ap.wps.com/l/cbCaip27oV0fhwjK","pdf",3698601,21,"English","# Abstract\n# Brain tumour background and motivation\n# Medical image interpretation and deep learning role\n# Proposed pipeline: pre-processing, segmentation, classification\n# Deep learning architectures and training setup\n# Experimental evaluation metrics and results\n# Literature review approach\n## Conventional models for brain tumour diagnosis","[{\"question\":\"Which deep learning models are evaluated for classification and segmentation?\",\"answer\":\"XceptionNet and MobileNet are evaluated for classification, while DeepLab is used for segmentation of the cancerous region.\"},{\"question\":\"What dataset is used to train and evaluate the models?\",\"answer\":\"All architectures are trained and evaluated using the BRATS 2018 dataset.\"},{\"question\":\"How is segmentation performance assessed, and what is the reported best result?\",\"answer\":\"Segmentation is assessed with Pearson Correlation Coefficient values, with DeepLab producing the best segmentation result (reported as 0.50/0.51 in the text) and additional agreement metrics.\"}]","Comprehensive brain tumour concealment utilizing peak valley filtering and deeplab segmentation | PDF",1790179071,53]