Detection of Leukemia using Image Processing
Ali Baig Mohammad1, P. Sainath2, K. Chiranjeevi Babu3, N. Chandra Mouli4
1Ali Baig Mohammad*, Assistant Professor, ECE Dept., Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist., A.P., India.
2P. Sainath, B.Tech. student, ECE Dept., Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist., A.P., India
3K Chiranjeevi Babu, B.Tech. student, ECE Dept., Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist., A.P., India
4N Chandra Mouli, B.Tech. student, ECE Dept., Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist., A.P., India
Manuscript received on November 14, 2019. | Revised Manuscript received on 25 November, 2019. | Manuscript published on December 10, 2019. | PP: 2914-2918 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7540129219/2019©BEIESP | DOI: 10.35940/ijitee.B7540.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In olden days, cancer detection at early stage and its prevention became a difficult task to predict and enhance the stage and efficient image techniques were not available. Due to wrong analysis of cancer presence, patients are treated wrongly. In our method, we detect acute myeloid leukemia effectively. After plotting data from image histogram plot and knowing the segmented region, area of cancer can be obtained by classification of cancer and non-cancer cells. Wrong diagnosis leads to patient’s death, so adequate steps are taken to make diagnosis accurate and to know the features. Effective usage of image tools and regions of infected part are extracted, and detection of cancer based on respective growth of region is observed which is 91 accurate.
Keywords: Blood Disorder Detection, Linear Distinction, Bar Graph Equalizing.
Scope of the Article: Image Processing and Pattern Recognition