Nithya, P and Uma Maheswari, B (2019) Liver Cancer Recognition and Categorization Based on Optimum Hierarchical Feature Fusion with Pesoa and DVW Technique. International Journal of Recent Technology and Engineering, 8 (2S8). pp. 1536-1540. ISSN 2277-3878

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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).

Item Type: Article
Uncontrolled Keywords: Data mining, liver cancer, classification, detection and PeSOA, DVW
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 12 Jul 2022 08:07
Last Modified: 12 Jul 2022 08:07

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