Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent | ||
| Engineering and Technology Journal | ||
| Article 14, Volume 38, 2A, February 2020, Pages 255-264 PDF (908.98 K) | ||
| DOI: 10.30684/etj.v38i2A.319 | ||
| Authors | ||
| Hanan A. R. Akkar1; Sameem A. Salman* 2 | ||
| 1Electrical Engineering Department, University of Technology, Baghdad, Iraq. 30080@uotechnology.edu.iq | ||
| 2Electrical Engineering Department, University of Technology, Baghdad, Iraq. bsceng2006@yahoo.com | ||
| Abstract | ||
| Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features. | ||
| Keywords | ||
| Bio-inspired Artificial Intelligent (AI); Moth Flame Optimization (MFO); Ant Colony Optimization (ACO); Particle Swarm Optimization (PSO) | ||
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