# Skin lesion segmentation and classification using deep learning and handcrafted features

arXiv: https://arxiv.org/abs/2112.10307 · HTML: https://mlsanity.com/paper/2112.10307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network](https://mlsanity.com/paper/1703.00577/) (2017)
- [Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features](https://mlsanity.com/paper/1807.07001/) (2018)
- [Skin Lesion Segmentation and Classification for ISIC 2018 by Combining Deep CNN and Handcrafted Features](https://mlsanity.com/paper/1908.05730/) (2019)

## Abstract (arXiv)

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.