Identification of acral melanoma using fuzzy algorithm compared with convolutional neural network for improved accuracy 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:Acral melanoma detection using a fuzzy method vs. convolutional neural network CNN utilizing dermoscopic pictures. Materials and Methods: To assess and compare the acral melanoma detection, the researchers used the fuzzy algorithm and the Convolutional neural network algorithm. The overall sample size is 40 due to the utilization of 20 samples acral melanoma is identified by evaluating the effectiveness with pre-test power of 80% (G-power) α=0.05, confidence level 95%. Result: The proposed fuzzy algorithm helps in increasing the higher accuracy compared to Convolutional Neural Network CNN with improved accuracy of the fuzzy algorithm algorithm is 80% and the CNN algorithm is 75 %. The accurate rate is 80with data features found in fuzzy algorithms and each algorithm has different precision. Conclusion: This study shows a higher accuracy for the fuzzy algorithm higher significant value than the convolutional neural network.

Imprint

V.Nithya lakshmi, P. Nirmala. Identification of Acral Melanoma using Fuzzy Algorithm compared with Convolutional Neural Network for improved accuracy using Dermoscopic Images. Cardiometry; Issue 25; December 2022; p.1627-1632; DOI: 10.18137/cardiometry.2022.25.16271632; Available from: https://www.cardiometry.net/issues/no25-december-2022/identification-acral-melanoma

Keywords

Acral Melanoma, Convolutional Neural Network, Innovative detection Technique, Machine Learning, Fuzzy Algorithm

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