Citation

Narjim, Sadia and Al Mamun, Abdullah Sarwar and Kundu, Diponkar (2020) Diagnosis of Acute Lymphoblastic Leukemia from Microscopic Image of Peripheral Blood Smear Using Image Processing Technique. Social Informatics and Telecommunications Engineering, 325. pp. 515-526. ISSN 1867-8211

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Abstract

At present, cancer is a second leading cause of death which rises the global burden. Among them acute lymphoblastic leukemia is a subtype of blood cancer which is most common in child as well as adults. It occurs when the number of lymphoblast is more producing from stem cells. Over time the accumulation of this abnormal cells in bone marrow prevents to produce other healthy blood cells in our body which is very dangerous. So, early detection is one of the most important which can increase patient’s survivability and treatment options. For cancer diagnosis, Ultrasound, Mammogram, MRI and microscopic images are some common methods used in medical science. Some basic detection processes of leukemia are CBC, PBS test and bone marrow test based on microscopic images. For blood cancer diagnosis, microscopic images are used manually which is time consuming and less accurate and can produce non standardized reports. So, it needs to detect leukemia automatically. Recently some computer aided methods are generated to diagnosis leukemia which are more reliable, more accurate, more precise and faster than manual diagnosis methods. In this paper a new automatic system has been proposed to detect all based on several image processing techniques from microscopic image of blood smear such as, segmentation, preprocessing, enhancement for getting better performance. To, classify blast cells and healthy cells ensemble classifier has been used with several types of feature such as, texture features, geometric features, statistic features. In this paper 99.1% accuracy, 98% Sensitivity have been achieved.

Item Type:	Article
Uncontrolled Keywords:	Image Processing, Acute Lymphoblastic Leukemia (ALL), WBCs count, Segmentation, Enhancement techniques, Feature extraction, Classification, MATLAB
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Management (FOM)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	28 Dec 2020 08:50
Last Modified:	28 Dec 2020 08:50
URII:	http://shdl.mmu.edu.my/id/eprint/7910

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