Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/444189
Title: | Automated detection and classification of leukaemia by image processing and deep learning techniques |
Researcher: | Anil Kumar, K K |
Guide(s): | Manoj, V J |
Keywords: | Convolutional neural networks Deep Learning Electronics and Communication Engineering and Technology Image Processing Leukaemia detection and classification |
University: | Cochin University of Science and Technology |
Completed Date: | 2021 |
Abstract: | Leukaemia is a cancer that originates in the blood forming tissues of bone marrow newlineand results in large number of abnormal white blood cells (WBC) in the bone newlinemarrow and blood. These immature abnormal cells, known as blasts, crowd out newlinenormal blood cells and prevent their development. There are mainly four newlineclassifications for leukaemia: Acute Lymphoblastic Leukaemia (ALL), Acute newlineMyeloid Leukaemia (AML), Chronic Lymphocytic Leukaemia (CLL) and Chronic newlineMyeloid Leukaemia (CML). Leukaemia is primarily diagnosed based on the signs newlineand symptoms of the patient, Complete Blood Count (CBC) test and peripheral newlineblood smear examination by pathologists using a microscope. A bone marrow newlineexamination and advanced laboratory tests are also carried out to confirm and newlineclassify leukaemia. The conventional blood and bone marrow smear examination by newlinelight microscopy suffers from intra-observer and inter-observer variability. Image newlineprocessing-based techniques which can automatically analyse the images of blood newlineand bone marrow smears to identify abnormal cells can overcome these personal newlinebiases of the medical professionals. This study uses Deep Learning based newlineclassification techniques for computer aided detection and classification of newlineleukaemia newline |
Pagination: | xxiv,213 |
URI: | http://hdl.handle.net/10603/444189 |
Appears in Departments: | Department of Electronics & Communication |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 538.65 kB | Adobe PDF | View/Open |
02_preliminary pages.pdf | 684.16 kB | Adobe PDF | View/Open | |
03_content.pdf | 472.21 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 394.26 kB | Adobe PDF | View/Open | |
05_chapter1.pdf | 842.33 kB | Adobe PDF | View/Open | |
06_chapter2.pdf | 864.82 kB | Adobe PDF | View/Open | |
07_chapter3.pdf | 943.84 kB | Adobe PDF | View/Open | |
08_chapter4.pdf | 1.12 MB | Adobe PDF | View/Open | |
09_chapter5.pdf | 1.75 MB | Adobe PDF | View/Open | |
10_chapter6.pdf | 1.28 MB | Adobe PDF | View/Open | |
11_chapter7.pdf | 919.34 kB | Adobe PDF | View/Open | |
12_chapter8.pdf | 1.22 MB | Adobe PDF | View/Open | |
13_chapter9.pdf | 430.17 kB | Adobe PDF | View/Open | |
14_annexures.pdf | 538.18 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 968.41 kB | Adobe PDF | View/Open |
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