[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122760-en":3,"doc-seo-122760-105":30,"detail-sidebar-cat-0-en-105":91},{"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},122760,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning based Early Stage Identification of Liver Tumor using Ultrasound Images","Liver cancer is a highly malignant disease that requires substantial diagnostic computation, and delayed or inaccurate identification can affect clinical outcomes. This work improves ultrasound-based liver tumor detection by adopting texture-oriented machine learning instead of relying mainly on color-based image classification. Ultrasound images are resized and pre-processed with Gaussian filtering, then texture features are extracted using GLCM and LBP. The features are classified with Multi SVM and K-NN, yielding higher precision, accuracy, sensitivity, and specificity than the existing approach.","Machine Learning based Early Stage Identification of Liver  Tumor using Ultrasound Images  \n S. Gandhimathi alias Usha a, * , S. Vasuki a, V.G. Janani a, B.H. Lavanya a, S. Seronica a  \nRESEARCH ARTICLE  \na Department of Electronics and Communication Engineering, Velammal College of Engineering and Technology, Madurai, Tamil Nadu, India.  \n* [Corresponding Author: ](Corresponding Author: ushadears@gmail.com)[ushadears@gmail.com](Corresponding Author: ushadears@gmail.com)  \nReceived: 26-01-2023, Revised: 05-05-2023, Accepted: 14-05-2023, Published: 30-05-2023  \nAbstract: Liver cancer is one of the most malignant diseases and its diagnosis requires more computational time. It can be minimized by applying a Machine learning algorithm for the diagnosis of cancer. The existing machine learning technique uses only the color-based methods to classify images which are not efficient. So, it is proposed to use texture-based classification for diagnosis. The input image is resized and pre-processed by Gaussian filters. The features are extracted by applying Gray level co-occurrence matrix (GLCM) and Local binary pattern (LBPin the preprocessed image. The Local Binary Pattern (LBP) is an efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. The extracted features are classified by multi-support vector machine (Multi SVM) and K-Nearest Neighbor (K-NN) algorithms. The Advantage of combining SVM with KNN is that SVM measures a large number of values whereas KNN accurately measures point values. The results obtained from the proposed techniques achieved high precision, accuracy, sensitivity and specificity than the existing method.  \nKeywords: Ultrasound imaging, Multi-Support vector machine, Kernel, GLCM, LBP  \n1. Introduction  \nCancer is a dangerous disease characterized by abnormal cell growth that has the potential to invade or spread to other parts of the body. Liver cancer is the most dangerous one [1] . For the diagnosis of liver cancer, computer-aided diagnosis (CAD) provides an output as a second opinion in order to assist radiologists in the diagnosis of various diseases on medical images. It is estimated that by the year 2022 around 830,180 people would die due to liver cancer. To help hepatologists and to improve diagnostic accuracy, machine learning techniques play a vital role in the diagnosis of liver cancer [2]. In the Ultrasound scanning technique, patients aren’t exposed to the ionizing radiations, making them safer than the diagnostic techniques such as X-rays and  \nCT scans. Ultrasound images provide high clarity images of soft tissues than the X-rays and CT scan images.  \nThe exact cause of liver cancer is associated with damage and scarring ofthe liver known as cirrhosis. The existing methodology, CAD system diagnoses liver cancer using the features of excrescence tumour attained from ultrasound images. Ultrasound images are used in the diagnosis of liver tissues, due to their ability to visualize human tissue exactly without deleterious effects [3] . The input data is transformed into a set of features called feature extraction. The characterization of liver images in this existing is based on texture analysis-based techniques [4]. There exist a considerable number of texture analysis techniques but GLCM is one of the finest algorithms. Texture analysis aims in finding a unique way of representing the underlying characteristics of textures and represent the feature values in some simpler but unique form so that they can be used for accurate classification [5]. The most common are first-order statistics, and gray level co-occurrence matrix (GLCM). It is a second-order statistics method, which is based on (local) information about gray levels in pair of pixels. The matrix is defined over the image with the distribution of co-occurring values of the given offset. Harlick described 22 statistical feature measures that can be","cbCaico2rLrmvLSI","https://ap.wps.com/l/cbCaico2rLrmvLSI","pdf",435943,1,11,"English","en",105,"# Introduction\n## Computer-aided diagnosis and ultrasound imaging context\n# Proposed Method\n## Preprocessing and feature extraction (GLCM, LBP)\n## Classification with Multi-SVM and K-NN","[{\"question\":\"Why use ultrasound images for early liver tumor identification?\",\"answer\":\"Ultrasound imaging is non-invasive and does not expose patients to ionizing radiation like X-rays or CT scans, while providing good clarity for soft tissues.\"},{\"question\":\"How are features extracted from the preprocessed ultrasound images?\",\"answer\":\"Images are resized and filtered with Gaussian filters, then texture features are extracted using the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP).\"},{\"question\":\"Which algorithms are used for classification, and what is the benefit of combining them?\",\"answer\":\"The extracted features are classified using Multi-SVM and K-NN. Combining SVM with K-NN is intended to leverage SVM’s ability to handle many values and K-NN’s accuracy for point values.\"}]","Machine Learning based Early Stage Identification of Liver Tumor using Ultrasound Images | PDF",1785812757,28,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-early-stage-identification-of-liver-tumor-using-ultrasound-images","",{"@graph":36,"@context":85},[37,54,68],{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-early-stage-identification-of-liver-tumor-using-ultrasound-images/122760/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why use ultrasound images for early liver tumor identification?","Question",{"text":75,"@type":76},"Ultrasound imaging is non-invasive and does not expose patients to ionizing radiation like X-rays or CT scans, while providing good clarity for soft tissues.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are features extracted from the preprocessed ultrasound images?",{"text":80,"@type":76},"Images are resized and filtered with Gaussian filters, then texture features are extracted using the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP).",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithms are used for classification, and what is the benefit of combining them?",{"text":84,"@type":76},"The extracted features are classified using Multi-SVM and K-NN. Combining SVM with K-NN is intended to leverage SVM’s ability to handle many values and K-NN’s accuracy for point values.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},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":106,"slug":138},19,"General","general"]