Skin Cancer Detection from Low-Resolution Images using Transfer Learning
Category:- Journal; Year:- 2021
Discipline:- Computer Science & Engineering Discipline
School:- Science, Engineering & Technology School
Skin cancer is one of the worst diseases noticed in humankind. It beholds some types, which even experts find challenging to categorize. In recent times, neural network-based automated systems have been entitled to perform this difficult task for their amazing ability of pattern recognition. However, the challenge remains due to the requirement for high-quality images and thus the necessity of highly-configured resources. In this research manuscript, the authors have addressed these issues. They pushed the boundary of neural networks by utilizing low-resolution (80x80, 64x64, and 32x32 pixel), highly imbalanced, grayscale HAM 10000 skin-cancer dataset into several pre-trained network architectures (VGG16, DenseNet169, DenseNet161, ResNet50) that have been successfully used for a similar purpose with high-resolution, augmented RGB HAM 10000 skin-cancer image dataset. The image resolution of the original HAM 10000 dataset is 800x600 p ixels. For the highest achieved performance for 80x80, 64x64, and 32x32 pixel images, were 80.46%, 78.56%, and 74.15%, respectively. All of these results were accomplished from the ImageNet pre-trained VGG16 model. The second-best model in terms of transfer learning was DenseNet169. The performances demonstrate that even within these severe circumstances, neural network-based transfer learning holds promising possibilities.