Combining VGG16 with Random Forest and Capsule Network for Detecting Multiple Myeloma

Swain, Deepak Kumar (2021) Combining VGG16 with Random Forest and Capsule Network for Detecting Multiple Myeloma. Masters thesis, Dublin, National College of Ireland.

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Abstract

Multiple Myeloma (MM) is a common blood cancer linked to white blood cells. Patients’ survival rates increase based on early diagnosis. Early diagnosis, however, has been a major issue. This study offers a model for detecting multiple myeloma from microscopic images of patients’ blood cells that combines VGG16, capsule networks (CapsNet), and random forests (RF). The model is trained using 85 blood cell pictures, and accuracy and intersection over union (IoU) metrics are used to compare it to state-of-the-art (SOTA) models like U-Net and masked-RCNN. From the comparison of the results of both models, it was observed that VGG16-CapsNet model gave better accuracy, but VGG16-RF model performed better in terms of segmentation of myeloma cells. However, the achieved segmented output was not better than the masked-RCNN model.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software Q Science > Life sciences > Medical sciences > Pathology > Tumors > Cancer H Social Sciences > HM Sociology > Information Science > Communication > Medical Informatics
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Clara Chan
Date Deposited:	15 Dec 2021 09:52
Last Modified:	15 Dec 2021 09:52
URI:	https://norma.ncirl.ie/id/eprint/5227

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