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Biomedical Image Segmentation Using Deep Learning

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dc.contributor.author Tabassum, Nabila
dc.contributor.author Reza, Md. Zahid
dc.contributor.author Dolamony, Nusrat Jahan
dc.date.accessioned 2021-11-30T04:17:33Z
dc.date.available 2021-11-30T04:17:33Z
dc.date.issued 2019-09-10
dc.identifier.uri http://dspace.ewubd.edu/xmlui/handle/123456789/3317
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh en_US
dc.description.abstract The process of solving medical issues by evaluating images created in clinical workout is known as medical image analysis.The goal is to obtain data for enhanced clinical diagnosis in an effective way. In this article presents a systematic overview of the present advance stage of medical image analysis using deep convolutional networks and also aims to develop automated methods for hippocampus brain MRI segmentation to decrease the time-consuming workload done by radiologist. We compared our results with hand-labeled segmentation done by medical radiologist. Our automated segmentation agreed well with human raters using a leave-one-out approach and standard overlap and distance error metrics,any differences were comparable with differences between trained human raters. Our error metrics compare favorably with those previously reported for other automated segmentations of hippocampus, suggesting the effectiveness of the approach to large-scale studies. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;ECE00211
dc.subject Biomedical Image Segmentation using Deep Learning en_US
dc.title Biomedical Image Segmentation Using Deep Learning en_US
dc.type Thesis en_US


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