[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120292-en":3,"doc-seo-120292-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120292,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",7,"Healthcare","Machine Learning based Segmentation for the Detection of Liver Disease","Artificial intelligence can modernize healthcare delivery by automating diagnostic workflows and enabling practitioners to process large-scale clinical data for timely medical guidance. This paper presents a segmentation-based liver tumor detection scheme using machine learning, then evaluates its performance with multiple classifiers under standard metrics such as accuracy, recall, precision, and F1-score. The study addresses the need for reliable tumor interpretation in medical imaging.","Machine Learning based Segmentation for the Detection of Liver Disease  \nParul Chhabraa, Pradeep Kumar Bhatiab  \naPhD Research Scholar, CSE Department, GJUS&T, Hisar, India  \nbProfessor, CSE Department, GJUS&T, Hisar, India  \n[a](aparul15march@gmail.com)[parul15march@gmail.com](aparul15march@gmail.com)  \n[b](bpkbhatia.gju@gmail.com)[pkbhatia.gju@gmail.com](bpkbhatia.gju@gmail.com)  \nABSTRACT- The support of Artificial intelligence (AI) can be used to update traditional healthcare services, and it can efficiently serve society. Using machine learning tools, the diagnosis process can be automated, and practitioners can process large scale clinical data to generate quick medical advisory for patients. In this paper, a segmentation based liver tumor detection scheme will be introduced, and its performance will be analyzed using different classifiers under the constraints of metric, i.e., accuracy, Recall, F1-Score, and Precision, etc.  \n.  \nKeywords-Machine Learning, Liver Tumor, Segmentation  \nI. INTRODUCTION  \nDisease recognition in medical imaging is quite complex, and specialists are required to examine it accurately. Improper observations may lead to an incorrect diagnosis. One of the most complex diseases is a Liver tumor, as shown in Figure 1.  \nFigure: 1 Liver tumor  \nIn this disease, Liver cells may grow abnormally, and it becomes very challenging to distinguish between healthy cells and affected cells. And error-prone interpretation directly affects the treatment plan.  \nThere are numerous issues associated with the Liver tumor detection process, which are described below:  \n➢ Disease detection: The tumor may grow in several years, and its detection at early stages is a major challenge.  \n➢ Parameter Identification: In different patients, tumor attributes (location, size, and growth interval) may vary. So, its accurate detection is necessary for decision making.  \n➢ Disease Categorization: Tumor categorization is another major issue that may affect the diagnosis process.  \nMachine learning (ML) approach can be used for the automation of disease detection/diagnosis process, but the following are the limitations for the ML-based solutions as shown in figure 2:  \nFigure: 2 Limitations of machine learning approaches  \n➢ Training dataset: A training dataset is required to build a training model that is used by ML schemes. It is prepared by a medical specialist, but its sample collection accuracy depends on the knowledge and experience in the relevant domain.  \n➢ Data Validation: Validation of the collected knowledge base is another major issue.  \n➢ Lack of Standard Solutions: For different diseases, there is a need to build training models as well as ML logic because there is no single remedy exists for all diseases.  \n➢ Dataset Volume: Training datasets may be quite large, so it may increase the infrastructure/processing cost for the implementation of ML solutions.  \n➢ Dataset Design: Various researchers are engaged in ML research in medical imaging. There is a need to define some sort of standards to design the input datasets [1-5] .  \nLiver tumor detection is a complex process, and analysis of its growth over a certain period is very critical. To achieve this goal, this paper introduces a machine learning method using active contour based segmentation to analyze the tumor in sample input.  \nII. LITERATURE SURVEY  \nM. D. Samad et al. [12] have machine learning based methods that can predict survival accuracy using a limited set of input variables for echocardiography outcomes. The study found that traditional methods use Ejection Fraction and Comorbidities based prediction models, which are less accurate as compared to machine learning algorithms.  \nY. Xue et al. [13] investigated different prediction models (Regression/Support Vector Machine/Random Tree based) for patient readmission, and analytical results indicate that the Functional Independence Measure method outperforms as compared to traditional methods. Training and","cbCaihgXnC7eqU14","https://ap.wps.com/l/cbCaihgXnC7eqU14","pdf",1505631,1,6,"English","en",105,"# Abstract\n# Introduction\n## Challenges in Liver Tumor Detection\n## Limitations of ML-based Solutions\n## Proposed Approach\n# Literature Survey","[{\"question\":\"What problem does the paper address in medical imaging?\",\"answer\":\"The paper targets liver tumor detection, where distinguishing healthy versus affected liver cells is difficult and error-prone interpretations can affect treatment decisions.\"},{\"question\":\"What method does the paper propose for liver tumor detection?\",\"answer\":\"It proposes a machine learning approach using active contour based segmentation to analyze tumors from input samples.\"},{\"question\":\"How is the proposed segmentation system evaluated?\",\"answer\":\"Performance is assessed using different classifiers with standard metrics including accuracy, recall, precision, and F1-score.\"}]","Machine Learning based Segmentation for the Detection of Liver Disease | PDF",1785729277,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-based-segmentation-for-the-detection-of-liver-disease","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-segmentation-for-the-detection-of-liver-disease/120292/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address in medical imaging?","Question",{"text":76,"@type":77},"The paper targets liver tumor detection, where distinguishing healthy versus affected liver cells is difficult and error-prone interpretations can affect treatment decisions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What method does the paper propose for liver tumor detection?",{"text":81,"@type":77},"It proposes a machine learning approach using active contour based segmentation to analyze tumors from input samples.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the proposed segmentation system evaluated?",{"text":85,"@type":77},"Performance is assessed using different classifiers with standard metrics including accuracy, recall, precision, and F1-score.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]