Enhancing Tumor Segmentation in 3D MRI Scans with YOLOv8 And Volumetric segmentation: A Computational Efficient Approach / (Record no. 607195)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02459nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Abdullah |
| 245 ## - TITLE STATEMENT | |
| Title | Enhancing Tumor Segmentation in 3D MRI Scans with YOLOv8 And Volumetric segmentation: A Computational Efficient Approach / |
| Statement of responsibility, etc. | Abdullah |
| 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 | 71p. ; |
| Dimensions | 30cm. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The computational aspect of this field also faces challenges. The high resolution and<br/>large data volumes of images require resources for processing. This does not require<br/>infrastructure but can also lead to delays in diagnosis and treatment planning, which is not ideal<br/>for medical professionals. Additionally, the costs associated with maintaining and acquiring<br/>systems pose a dilemma for healthcare organizations and research institutions.<br/>Even when computational challenges are addressed network designs like CNNs have their<br/>limitations. They may struggle to capture all the details in an image and techniques, like pooling<br/>can result in the loss of data.<br/>This research aims to overcome these challenges by focusing on optimizing the analysis of brain<br/>tumor images. We place importance on reducing the burden while still capturing crucial tumor<br/>specific information. To achieve this we introduced a pipeline that ensures reproducible models<br/>by standardizing the data.<br/>Our methodology is based on combining data processing, computer vision and learning<br/>techniques. We have innovatively integrated the YOLOv8 model to enable tumor localization<br/>and prediction in unexamined imaging datasets. Through the technique of data stacking we<br/>create three representations of MRI scans along with their masks. Training a model on these<br/>stacked data promises utilization of resources while ensuring accurate tumor predictions.<br/>In summary this study sheds light on how to enhance the methods of identifying tumors in MRI<br/>scans. It combines data processing, computer vision and deep learning techniques using the<br/>YOLOv8 model as a foundation. The discoveries made here have implications, for advancing<br/>medical image analysis. The goal is to achieve more tumor detection, which can greatly impact<br/>diagnosis and treatment processes.<br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Kashif Javed |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/39757">http://10.250.8.41:8080/xmlui/handle/123456789/39757</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 | 12/06/2023 | 629.8 | SMME-TH-941 | Thesis |