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040 _aAIKTC-KRRC
_cAIKTC-KRRC
100 _919997
_aSwami, Someshwar A
245 _aDetection of white blood cells using convolutional neural network
250 _aVol.12(6), Nov-Dec
260 _aHaryana
_bIOSR - International Organization of Scientific Research
_c2022
300 _a14-20p.
520 _aIn this work, image processing and deep learning mechanisms are used to locate and classify the White Blood Cells based on their categories. The White Blood Cells which are classified are counted and compared with the standard range of the types available in the human blood sample. By comparing the availability of White Blood Cells types, the normal and the abnormal blood samples are predicted accordingly. The dataset of the normal blood sample is obtained from the laboratory in biotechnology department and the datasets used for training in Convolutional Neural Network are attained from the website Leukocyte Images for Segmentation and Classification (LISC). This will increase efficiency and reduce the doctor’s burden as traditional manual counting is dull, tedious, and possibly subjective. Background: White Blood Cells (WBCs) are also called leukocytes or leucocytes. These are cells of the immune system that are involved in the body against both infectious disease and foreign materials. These cells help fight infections by attacking bacteria, viruses, and germs that invade the body. All leukocytes are produced and derived from a multipotentcell. This is also known as a hematopoietic stem cell. They live for about three to four days in the human body. The segmentation has to be done to extract the nucleus of white blood cell image.
650 0 _94622
_aComputer Engineering
700 _919998
_aDabir, R. S.
773 0 _tIOSR journal of VLSI and signal processing (IOSR-JVSP)
_dGurgaon International Organization Of Scientific Research (IOSR)
_x2319 – 4197
856 _uhttps://www.iosrjournals.org/iosr-jvlsi/papers/vol12-issue6/B12061420.pdf
_yClick here
942 _2ddc
_cAR