A Smartphone Application for Skin Lesion Detection and Classification with Deep Learning Algorithms

Authors

  • X.Anitha Mary Department of Robotics Engineering, Karunya Institute of Technology and Sciences,Coimbatore https://orcid.org/0000-0002-7284-1541
  • Peniel Winifred Department of Robotics Engineering, Karunya Institute of Technology and Sciences,Coimbatore
  • C. Suganthi Evangeline Department of Electronics and Communication Engineering, KITS
  • Vinoth Babu Kumaravelu School of Electronics Engineering, VIT, Vellore, Tamil Nadu, India https://orcid.org/0000-0002-1778-9891
  • C. Karthik Associate Professor, Department of Robotics and Automation, Jyothi Engineering College, Thrissur, Kerala
  • Subrata Chowdhury Associate Professor, Department of MCA,Sri Venkateswara college of Engineering and Technology, Chittoor, Andhra Pradesh, India
  • Khaled Rabie Professor, Department of Engineering, Manchester Metropolitan University, United Kingdom
  • Bui Thi Thu Institute of Information Technology, Vietnam Academy of Science and Technology, Haoni, Vietnam
  • Vu Duong Tung Institute of Information Technology, Vietnam Academy of Science and Technology, Haoni, Vietnam

Keywords:

Application development, Customized model, Deep models, Skin lesion classification, Tensor Flow Lite (TFL), Validation accuracy

Abstract

The Skin Lesion (SL) classification has recently received a lot of attention.  Because of the significant resemblance between these skin lesions, physicians spend a lot of time analyzing them. A Deep Learning (DL) based automated categorization system can help clinicians recognize the type of SL and improve the patient's health. In this research, DL approaches such as VGG-16, ResNet-50 and customized model are employed to detect the SL using a smartphone application. These models are trained on the SL classification dataset from the International Skin Imaging Collaboration (ISIC) 2019. The customized model over fits the other two models with a validation accuracy of 86.21%, whereas the validation accuracy of VGG-16 and ResNet-50 is 85.15% and 84.82%, respectively. Physicians will save time and have a higher precision rate in the automatic classification of SL utilizing DL.

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Block diagram for classification of SL

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Published

01.10.2022

How to Cite

[1]
X. . Mary, “A Smartphone Application for Skin Lesion Detection and Classification with Deep Learning Algorithms”, Int J Intell Syst Appl Eng, vol. 10, no. 3, pp. 34–39, Oct. 2022.

Issue

Vol. 10 No. 3 (2022)

Section

Research Article

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