Skin Lesion Classification Using CNN-based Transfer Learning Model

The computer-aided diagnosis (CAD) and the analysis of skin lesions using deep learning models have become common in the last decade. The proposed CAD systems have considered various datasets and deep learning models. The transfer of knowledge from particular pre-trained models to others has also gained importance due to the efficient convergence and superior results. This study presents the design and implementation of a transfer learning model using Convolutional Neural Networks (CNN) with variable training epoch numbers to classify skin lesion images obtained by smartphones. The model is divided into the inner and external CNN models to train and transfer the knowledge, and the preprocessing and data augmentation are not applied. Several experiments are performed to classify cancerous and non-cancerous skin lesions and all skin lesion types provided in the dataset separately. The designed model increased the classification rates by 20% compared to the conventional CNN. The transfer learning model achieved 0.81, 0.88, and 0.86 mean recall, mean specificity, and mean accuracy in detecting cancerous lesions, and 0.83, 0.90, and 0.86 macro recall, macro precision, and macro F1 score in classifying six skin lesions. The obtained results show the efficacy of transfer learning in skin lesion diagnosis.

Automated summary

In the past decade, the use of computer-aided diagnosis (CAD) and deep learning models for skin lesion analysis has become common. This study presents a transfer learning model using Convolutional Neural Networks (CNN) to classify skin lesion images obtained by smartphones. The designed model increased the classification rates by 20% compared to conventional CNN. The transfer learning model achieved high recall, specificity, and accuracy in detecting cancerous lesions, and high recall, precision, and F1 score in classifying six skin lesions, demonstrating the efficacy of transfer learning in skin lesion diagnosis.