Identification of acral melanoma using genetic algorithms compared with convolutional neural network using dermoscopic images

V.Nithya Lakshmi¹, P. Nirmala¹

¹ Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical & Technical Sciences, Saveetha University, Chennai, Tamilnadu, India

Abstract

Aim: Identification of acral melanoma using genetic algorithm compared with convolutional neural network CNN using dermoscopic images. Materials and Methods: The study was conducted using the genetic algorithm and convolutional neural network algorithm to analyze and compare the acral melanoma detection. The number of samples used is 20, total sample size is 40. Acral melanoma is identified by evaluating the effectiveness with pre-test power of 80% (G-power), α=0.05, confidence interval 95%. Result: The proposed genetic algorithm helps in increasing the higher accuracy compared to convolutional neural networks with improved accuracy of the genetic algorithm algorithm is 96 % and the convolutional neural network algorithm is 95%. The accurate rate is 80 with the data features found in the genetic algorithm algorithm. Precision is different in each algorithm. Conclusion: This study shows a higher accuracy for the genetic algorithm when compared with convolutional neural networks.

Imprint

V.Nithya Lakshmi, P. Nirmala. Identification of Acral Melanoma using Genetic Algorithms Compared with Convolutional Neural Network using Dermoscopic Images. Cardiometry; Issue 25; December 2022; p.1640-1645; DOI: 10.18137/cardiometry.2022.25.16401645; Available from: https://www.cardiometry.net/issues/no25-december-2022/genetic-algorithms-compared

Keywords

Acral Melanoma, Convolutional Neural Network CNN, Genetic Algorithm, Innovative Technique, Machine Learning, Skin Cancer

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