Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/544654
Full metadata record
DC FieldValueLanguage
dc.date.accessioned2024-02-09T08:30:42Z-
dc.date.available2024-02-09T08:30:42Z-
dc.identifier.urihttp://hdl.handle.net/10603/544654-
dc.description.abstractIn medical imaging, machine learning (ML) and deep learning (DL) play a significant role to predict symptoms of early diseases. DL is one of the growing trends in general data analysis since 2013. It is an improvement of artificial neural networks (ANN) as it consists of many hidden layers to permit high level of abstraction from data. In particular, convolutional neural networks (CNNs) have proven to be a potential tool for computer vision tasks. Deep CNN shave a capability to automatically learn raw data especially images. The accurate assessment or identification of disease depends on image interpretation as well as acquisition. Due to the improvement in last decade in image acquisition, devices acquire data at high rate with increased resolution. However, the interpretation of images has recentlybeguntobenefitfromcomputertechnology.Mostlythesearemadebytheradiologist, physicians and senior doctors but limited due to its subjectivity and high skilled physicians/doctors. Computerized tools in the medical imaging field are the key enablers to improve diagnosis by facilitating the findings. Analysis of thyroid ultrasonography (USG)images via visual inspection and manual examination for early identification and classification of thyroid nodule has always been cumbersome. This manual examination of thyroid USG images in order to identify benign and malignant thyroid nodule can be tedious and time-consuming. Various deep learning models have emerged in medical field especially in thyroid nodule classification with the rapid advancement in technology and increase in computational resources. Early identification of such nodules can improve the effectiveness of clinical interventions and treatments. Therefore, many researchers now advocate the use of computer diagnostic system (CDS) to objectively and quantitatively analyze the USG images of thyroid nodules. This helps the radiologists to solve the differences in interpretation of results.-
dc.format.extentxviii, 89p.-
dc.languageEnglish-
dc.rightsuniversity-
dc.titleEarly Identification and Classification of Thyroid Nodule in Medical Ultrasound Images-
dc.creator.researcherSrivastava, Rajshree-
dc.subject.keywordComputer Science-
dc.subject.keywordDiagnostic ultrasonic imaging-
dc.subject.keywordEngineering and Technology-
dc.subject.keywordImaging Science and Photographic Technology-
dc.subject.keywordLymph nodes-
dc.subject.keywordMachine learning-
dc.subject.keywordMalignant carcinoid syndrome-
dc.subject.keywordThyroid gland-
dc.contributor.guideKumar, Pardeep-
dc.publisher.placeSolan-
dc.publisher.universityJaypee University of Information Technology, Solan-
dc.publisher.institutionDepartment of Computer Science Engineering-
dc.date.registered2019-
dc.date.completed2024-
dc.date.awarded2024-
dc.format.accompanyingmaterialDVD-
dc.source.universityUniversity-
dc.type.degreePh.D.-
Appears in Departments:Department of Computer Science Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File27.17 kBAdobe PDFView/Open
02_prelim pages.pdf685.27 kBAdobe PDFView/Open
03_content.pdf196.13 kBAdobe PDFView/Open
04_abstract.pdf32.92 kBAdobe PDFView/Open
05_chapter 1.pdf210.53 kBAdobe PDFView/Open
06_chapter 2.pdf282.06 kBAdobe PDFView/Open
07_chapter 3.pdf304.33 kBAdobe PDFView/Open
08_chapter 4.pdf584.61 kBAdobe PDFView/Open
09_chapter5.pdf570.13 kBAdobe PDFView/Open
10_chapter 6.pdf703.14 kBAdobe PDFView/Open
11_annexures.pdf372.22 kBAdobe PDFView/Open
80_recommendation.pdf51.1 kBAdobe PDFView/Open


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: