Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/486206
Title: | An Effective Diagnostic Approach Of Lungs Cancer Using Data Mining |
Researcher: | Vinod Kumar |
Guide(s): | Brijesh Bakariya |
Keywords: | Computer Science Computer Science Artificial Intelligence Engineering and Technology |
University: | I. K. Gujral Punjab Technical University |
Completed Date: | 2023 |
Abstract: | Lung cancer is the major cause of death for millions of people worldwide. More than any other newlinetype of cancer, the mortality rate from lung disease is the highest. As a result, the chances of newlinesurvival from lung cancer are particularly dependent on its stage at the time of detection. Early newlinedetection is the most effective way to reduce their risk [1]. As lung cancer progresses and becomes newlinemore aggressive, staging is crucial to predicting survival and determining the most appropriate newlinepath to therapy. There are several imaging techniques available to identify lung cancer, so CT newlineimage is the most commonly used imaging method today newline |
Pagination: | All pages |
URI: | http://hdl.handle.net/10603/486206 |
Appears in Departments: | Department of Computer Science and Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 646.75 kB | Adobe PDF | View/Open |
02_prelim page.pdf | 926.82 kB | Adobe PDF | View/Open | |
03_content.pdf | 509.47 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 263.5 kB | Adobe PDF | View/Open | |
05_chapter1.pdf | 1.77 MB | Adobe PDF | View/Open | |
06_chapter2.pdf | 2.02 MB | Adobe PDF | View/Open | |
07_chapter3.pdf | 2.36 MB | Adobe PDF | View/Open | |
08_chapter4.pdf | 2.71 MB | Adobe PDF | View/Open | |
09_chapter5.pdf | 2.39 MB | Adobe PDF | View/Open | |
10_annexure.pdf | 411.46 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 380.6 kB | Adobe PDF | View/Open |
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