Multimodal Segmentation of Brain tumor using BraTS dataset 2020 / (Record no. 607365)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02025nam a22001697a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Saeed, Aniqa |
| 245 ## - TITLE STATEMENT | |
| Title | Multimodal Segmentation of Brain tumor using BraTS dataset 2020 / |
| Statement of responsibility, etc. | Aniqa Saeed |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 60p. ; |
| Other physical details | Soft Copy |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | BRaTS’20 dataset aims for better understanding and developing an AI-based approach<br/>with novelty for multimodal segmentation of brain tumor using MRI images that are<br/>already in use since 2015 for better and accurate diagnosis of brain tumor. Pre-operative<br/>multimodal MRI scans of glioblastoma (GBM/HGG) and lower grade glioma (LGG),<br/>with pathologically confirmed diagnosis are available for each year where AI students are<br/>welcomed for challenges to develop novel models. These datasets contain training,<br/>validation and testing data for respective year’s BraTS challenge. Our study involve<br/>automated segmentation using SegResNet model for 3T multimodal MRI scans of<br/>recently provided BraTS dataset 2020. Our model has been designed based on the<br/>encoder-decoder structure and is able to achieve a 0.90 mean dice score on training set<br/>and 0.87 on the validation set. Experimental results on the testing set demonstrate no over<br/>or under fitting and is able to achieve average dice scores of 0.9000, 0.8911 and 0.8426<br/>for the tumor core, whole tumor and enhancing tumor respectively. The proposed BraTS<br/>model underwent through some specific modifications that created novelty comparing<br/>datasets and models of previous benchmarks.Our approach has surpassed the previous<br/>models of BraTS’20 dataset in many ways giving highest dice scores for tumor core and<br/>enhancing tumor while second highest for whole tumor.<br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Sciences (BMS) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Amer Sohail Kashif |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/33947">http://10.250.8.41:8080/xmlui/handle/123456789/33947</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 12/13/2023 | 610 | SMME-TH-856 | Thesis |