Skin Cancer Classification using Inception Network and Transfer Learning
This work addresses medical image classification for skin cancer diagnosis, but it is incremental as it applies an existing method to a specific dataset.
The paper tackled skin cancer classification on the imbalanced HAM10000 dataset using a pretrained convolutional neural network, achieving good precision with low resource requirements.
Medical data classification is typically a challenging task due to imbalance between classes. In this paper, we propose an approach to classify dermatoscopic images from HAM10000 (Human Against Machine with 10000 training images) dataset, consisting of seven imbalanced types of skin lesions, with good precision and low resources requirements. Classification is done by using a pretrained convolutional neural network. We evaluate the accuracy and performance of the proposal and illustrate possible extensions.