A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system

Douglas de A. Rodrigues, Roberto F. Ivo, Suresh Chandra Satapathy, Shuihua Wang, Jude Hemanth*, Pedro P.Rebouças Filho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

80 Citations (Scopus)


Melanoma skin cancer is one of the most common diseases in the world. It is essential to diagnose melanoma at an early stage. Visual inspection during the medical examination of skin lesions is not a simple task, as there is a similarity between lesions. Also, medical experience and disposition can result in inaccurate diagnoses. Technologies such as the Internet of Things (IoT) have helped to create effective health systems. Doctors can use them anywhere, with the guarantee that more people can be diagnosed without prejudice to subjective factors. Transfer Learning and Deep Learning are increasingly significant in the clinical diagnosis of different diseases. This work proposes the use of Transfer Learning and Deep Learning in an IoT system to assist doctors in the diagnosis of common skin lesions, typical nevi, and melanoma. This work uses Convolutional Neural Networks (CNNs) as resource extractors. The CNN models used were: Visual Geometry Group (VGG), Inception, Residual Networks (ResNet), Inception-ResNet, Extreme Inception (Xception), MobileNet, Dense Convolutional Network (DenseNet), and Neural Architecture Search Network (NASNet). For the classification of injuries, the Bayes, Support Vector Machines (SVM), Random Forest (RF), Perceptron Multilayer (MLP), and the K-Nearest Neighbors (KNN) classifiers are used. This study used two datasets: the first provided by the International Skin Imaging Collaboration (ISIC) at the International Biomedical Imaging Symposium (ISBI); the second is PH2. For ISBI-ISIC, this study examined lesions between nevi and melanomas. In PH2, this work analyzed the diagnosis based on lesions of common nevus, atypical nevi, and melanomas. The DenseNet201 extraction model, combined with the KNN classifier achieved an accuracy of 96.805% for the ISBI-ISIC dataset and 93.167% for the PH2. Thus, an approach focused on the IoT system is reliable and efficient for doctors who assist in the diagnosis of skin lesions.

Original languageEnglish
Pages (from-to)8-15
Number of pages8
JournalPattern Recognition Letters
Publication statusPublished - Aug 2020
Externally publishedYes


  • CNN
  • Internet of things
  • Skin lesion
  • Transfer learning


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