Low-cost and high-performance data augmentation for deep-learning-based skin lesion classification

by   Shuwei Shen, et al.

Although deep convolutional neural networks (DCNNs) have achieved significant accuracy in skin lesion classification comparable or even superior to those of dermatologists, practical implementation of these models for skin cancer screening in low resource settings is hindered by their limitations in computational cost and training dataset. To overcome these limitations, we propose a low-cost and high-performance data augmentation strategy that includes two consecutive stages of augmentation search and network search. At the augmentation search stage, the augmentation strategy is optimized in the search space of Low-Cost-Augment (LCA) under the criteria of balanced accuracy (BACC) with 5-fold cross validation. At the network search stage, the DCNNs are fine-tuned with the full training set in order to select the model with the highest BACC. The efficiency of the proposed data augmentation strategy is verified on the HAM10000 dataset using EfficientNets as a baseline. With the proposed strategy, we are able to reduce the search space to 60 and achieve a high BACC of 0.853 by using a single DCNN model without external database, suitable to be implemented in mobile devices for DCNN-based skin lesion detection in low resource settings.


page 2

page 3


Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification

Deep convolutional neural network (DCNN) models have been widely explore...

Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning

In this report, we introduce the outline of our system in Task 3: Diseas...

Data Augmentation for Skin Lesion Analysis

Deep learning models show remarkable results in automated skin lesion an...

Dynamic hardware system for cascade SVM classification of melanoma

Melanoma is the most dangerous form of skin cancer, which is responsible...

Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation

The classification of glomerular lesions is a routine and essential task...

Efficient Deep Learning Architectures for Fast Identification of Bacterial Strains in Resource-Constrained Devices

This work presents twelve fine-tuned deep learning architectures to solv...

Please sign up or login with your details

Forgot password? Click here to reset