A study on skin tumor classification based on dense convolutional networks with fused metadata

Wenjun Yin; Jianhua Huang; Jianlin Chen; Yuanfa Ji

doi:10.3389/fonc.2022.989894

A study on skin tumor classification based on dense convolutional networks with fused metadata

Front Oncol. 2022 Dec 16:12:989894. doi: 10.3389/fonc.2022.989894. eCollection 2022.

Authors

Wenjun Yin¹, Jianhua Huang¹, Jianlin Chen², Yuanfa Ji¹

Affiliations

¹ School of Information and Communication, Guilin University Of Electronic Technology, Guilin, China.
² Reproductive Endocrinology Clinic, Second Xiangya Hospital of Central South University, Changsha, China.

Abstract

Skin cancer is the most common cause of death in humans. Statistics show that competent dermatologists have a diagnostic accuracy rate of less than 80%, while inexperienced dermatologists have a diagnostic accuracy rate of less than 60%. The higher rate of misdiagnosis will cause many patients to miss the most effective treatment window, risking the patients' life safety. However, the majority of the current study of neural network-based skin cancer diagnosis remains at the image level without patient clinical data. A deep convolutional network incorporating clinical patient metadata of skin cancer is presented to realize the classification model of skin cancer in order to further increase the accuracy of skin cancer diagnosis. There are three basic steps in the approach. First, the high-level features (edge features, color features, texture features, form features, etc.). Implied by the image were retrieved using the pre-trained DenseNet-169 model on the ImageNet dataset. Second, the MetaNet module is introduced, which uses metadata to control a certain portion of each feature channel in the DenseNet-169 network in order to produce weighted features. The MetaBlock module was added at the same time to improve the features retrieved from photos using metadata, choosing the most pertinent characteristics in accordance with the metadata data. The features of the MetaNet and MetaBlock modules were finally combined to create the MD-Net module, which was then used as input into the classifier to get the classification results for skin cancers. On the PAD-UFES-20 and ISIC 2019 datasets, the suggested methodology was assessed. The DenseNet-169 network model combined with this module, according to experimental data, obtains 81.4% in the balancing accuracy index, and its diagnostic accuracy is up between 8% and 15.6% compared to earlier efforts. Additionally, it solves the problem of actinic keratosis and poorly classified skin fibromas.

Keywords: CNNs; DenseNet-169 model; feature fusion; metadata; skin tumor classification.