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SLC_ShiftingEnsemble

This repository contains the source code of the paper Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting.

This code executes the Shifted GoogLeNet+MobileNetV2 method for the HAM10000 dataset. The contents of this code are provided without any warranty. They are intended for evaluational purposes only.

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Pre-requisites

  • Matlab (tested on v2020b or earlier). Deep learning toolbox is required to load GoogLeNet and MobileNetV2.

Training

  1. Open trainNets.m and set up the paths of the dataset
  2. Run the script

Testing

  1. Open testNetGrids.m and set up the paths of the dataset
  2. Run the script

Evaluation

  • computeStatsCV.m: computes the statistics of the 10-fols CV
  • plotConfusionCV.m: computes the confusion matrices of the tested models
  • plotModelsComparisonCV.m: plots the graph bar comparing all models

Citation

Please, cite this work as:

K. Thurnhofer-Hemsi, E. López-Rubio, E. Domínguez and D. A. Elizondo, "Skin Lesion Classification by Ensembles of Deep Convolutional Networks and Regularly Spaced Shifting", in IEEE Access, vol. 9, pp. 112193-112205, 2021, doi: 10.1109/ACCESS.2021.3103410. (https://ieeexplore.ieee.org/abstract/document/9508981)