Skin lesion segmentation and classification using deep learning and handcrafted features
This work addresses incremental improvements in diagnostic accuracy for skin lesion classification in medical imaging.
The paper tackled skin lesion classification by injecting handcrafted features into a CNN during training, achieving a 92.3% balanced multiclass accuracy, which is 6.8% better than typical single-method classifiers.
Accurate diagnostics of a skin lesion is a critical task in classification dermoscopic images. In this research, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single method features. This study involves a new technique where we inject the handcrafted features or feature transfer into the fully connected layer of Convolutional Neural Network (CNN) model during the training process. Based on our literature review until now, no study has examined or investigated the impact on classification performance by injecting the handcrafted features into the CNN model during the training process. In addition, we also investigated the impact of segmentation mask and its effect on the overall classification performance. Our model achieves an 92.3% balanced multiclass accuracy, which is 6.8% better than the typical single method classifier architecture for deep learning.