Using Semi-supervised Transfer Learning for Classification of Solar Lentigo, Lentigo Maligna, and Lentigo Maligna Melanoma

Using Semi-supervised Transfer Learning for Classification of Solar Lentigo, Lentigo Maligna, and Lentigo Maligna Melanoma

연구 분야: Artificial Intelligence

학회: 2022 14th Biomedical Engineering International Conference (BMEiCON)

Skin cancer is the most frequent malignancy worldwide, with the number of new cases increasing yearly. Computer-aided diagnosis from skin images has recently become a critical procedure to detect early melanoma stages before becoming metastasis. This study intended to classify three stages of skin cancer: solar lentigo (SL), lentigo maligna (LM), and lentigo maligna melanoma (LMM) using transfer learning and semi-supervised transfer learning of a convolutional neural network algorithm based on VGG-16 and VGG-19. Skin images were obtained from various databases, including labeled and unlabeled data, and were preprocessed using hair removal software and a data balancing technique. The image data were then trained in ten experiments: supervised learning, supervised transfer learning, and semi-supervised transfer learning using VGG-16 and VGG-19 with and without augmentation. The results show that supervised learning gives an accuracy of 0.47. Based on VGG-16 and VGG19 which are comparable in performance, the accuracies increase to 0.72 and 0.72 for supervised transfer learning, and 0.92 and 0.98 for semi-supervised transfer learning, respectively. However, when applying augmentation, the accuracies decrease. Therefore, the use of semi-supervised transfer learning based on VGG-19 gives the best prediction in our study.