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

Mary, X. ., Winifred, P. ., Evangeline, C. S. ., Kumaravelu, V. B. ., Karthik, C. ., Chowdhury, S. ., Rabie, K. ., Thu, B. T. ., & Tung, V. D. . (2022). A Smartphone Application for Skin Lesion Detection and Classification with Deep Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 10(3), 34–39. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2135

Issue

Vol. 10 No. 3 (2022)

Section

Research Article

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