Version 1
: Received: 14 September 2022 / Approved: 15 September 2022 / Online: 15 September 2022 (03:02:06 CEST)
How to cite:
Singha, S.; Roy, P. Skin Cancer Classification and Comparison of Pretrained Models Performance using Transfer Learning. Preprints2022, 2022090215. https://doi.org/10.20944/preprints202209.0215.v1
Singha, S.; Roy, P. Skin Cancer Classification and Comparison of Pretrained Models Performance using Transfer Learning. Preprints 2022, 2022090215. https://doi.org/10.20944/preprints202209.0215.v1
Singha, S.; Roy, P. Skin Cancer Classification and Comparison of Pretrained Models Performance using Transfer Learning. Preprints2022, 2022090215. https://doi.org/10.20944/preprints202209.0215.v1
APA Style
Singha, S., & Roy, P. (2022). Skin Cancer Classification and Comparison of Pretrained Models Performance using Transfer Learning. Preprints. https://doi.org/10.20944/preprints202209.0215.v1
Chicago/Turabian Style
Singha, S. and Priyangka Roy. 2022 "Skin Cancer Classification and Comparison of Pretrained Models Performance using Transfer Learning" Preprints. https://doi.org/10.20944/preprints202209.0215.v1
Abstract
Skin cancer is an uncommon but serious malignancy. Dermoscopic images examination and biopsy are required for cancer detection. Deep learning (DL) is extremely effective in learning characteristics and predicting malignancies. However, DL requires a large number of images to train. Image augmentation and transferring learning were employed to overcome the lack of images issue. In this study we divided images into two categories: benign and malignant. To train and test our models, we used the public ISIC 2020 database. Melanoma is classified as malignant in the ISIC 2020 dataset. Along with categorization, the dataset was studied to demonstrate variation. The performance of three top pretrained models was then benchmarked in terms of training and validation accuracy. Three optimizers were employed to optimize the loss: RMSProp, SGD, and ADAM. Using ResNet, VGG16, and MobileNetV2, we obtained training accuracy of 98.73%, 99.12%, and 99.76%, respectively. Using these three pretrained models, we attained a validation accuracy of 98.39%.
Keywords
pretrained model; transfer learning; skin cancer; deep learning; ISIC 2020
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
