Comparison of Three Deep Learning Models in Accurate Classification of 770 Dermoscopy Skin Lesion Images

Abstract

Accurately determining and classifying different types of skin cancers is critical for early diagnosis. In this work, we propose a novel use of deep learning for classification of benign and malignant skin lesions using dermoscopy images. We obtained 770 de-identified dermoscopy images from the University of Missouri (MU) Healthcare. We created three unique image datasets that contained the original images and images obtained after applying a hair removal algorithm. We trained three popular deep learning models, namely, ResNet50, DenseNet121, and Inception-V3. We evaluated the accuracy and the area under the curve (AUC) receiver operating characteristic (ROC) for each model and dataset. DenseNet121 achieved the best accuracy (80.52%) and AUC ROC score (0.81) on the third dataset. For this dataset, the sensitivity and specificity were 0.80 and 0.81, respectively. We also present the SHAP (SHapley Additive exPlanations) values for the predictions made by different models to understand their interpretability.

Figure 1:

Skin lesion examples

Figure 2:

Impact of applying the hair removal algorithm

Figure 3:

Our overall approach for skin lesion classification

Figure 4:

Test images selected for computing SHAP values and predictions made by DenseNet121 for these images on different datasets

Figure 5:

SHAP values for a few test images in the three datasets