Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/423334
Title: | An Efficient Tumor Detection Framework Using Deep Learning |
Researcher: | Damandeep |
Guide(s): | Surender Singh and Kavita |
Keywords: | Computer Science Computer Science Artificial Intelligence Engineering and Technology |
University: | Chandigarh University |
Completed Date: | 2022 |
Abstract: | Brain tumor is the 22nd most common cancer worldwide with 1.8% of the total number of new cancers. Brain tumor Segmentation ad classification plays vital role in Tumor diagnosis using various image processing techniques, As Brain tumor is a critical and life threatening condition which is spreading worldwide. So Early Detection of Brain tumor can improve the patient Survival. Because of their enormous increases in data search and extraction speed and accuracy, as well as individualized treatment suggestions, machine- and deep-learning techniques are being increasingly commonly applied throughout healthcare industries. newline |
Pagination: | |
URI: | http://hdl.handle.net/10603/423334 |
Appears in Departments: | Department of Computer Science Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 129.07 kB | Adobe PDF | View/Open |
02_prelim page.pdf | 513.78 kB | Adobe PDF | View/Open | |
03_content.pdf | 201.73 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 78.11 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 673.66 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 179.07 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 159.64 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 940.97 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 565.72 kB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 347.95 kB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 90 kB | Adobe PDF | View/Open | |
12_annexure.pdf | 256.9 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 217.72 kB | Adobe PDF | View/Open |
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