Liver Cancer Recognition and Categorization Based on Optimum Hierarchical Feature Fusion with Pesoa and DVW Technique
P. Nithya1, B. Uma Maheswari2
1Dr. P. Nithya, Associate Professor, Department of Computer Technology, PSG College of Arts & Science, Coimbatore (Tamil Nadu), India.
2Dr. B. Uma Maheswari, Assistant Professor, Department of Computer Applications, PSG College of Arts & Science, Coimbatore (Tamil Nadu), India.
Manuscript received on 22 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1536-1540 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B10990882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1099.0882S819
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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: Liver malignant growth extends the demise rate on the grounds that the symptoms can’t be recognized even the disease is in its propelled stage. The early analysis and steady watching is the most ideal approach to control the advancement of the harm and to spare the lives. Ultrasound imaging is a champion among the most as often as possible used determination instruments to recognize and characterize inconsistencies of the liver which is likewise a non-obtrusive, safe procedure for patient examination, being anything but difficult to apply, efficient than the CT, MRI, PET based liver tumor recognition. Conventional liver disease recognition systems have high calculation time and multifaceted nature. So as to decrease the multifaceted nature in the computational method and to upgrade the symptomatic precision in this paper we propose another ideal progressive component combination dependent on Penguin Search Optimization Algorithm (PeSOA).
Keywords: Data Mining, Liver Cancer, Classification, Detection and PeSOA, DVW.
Scope of the Article: Pattern Recognition