Development of a light‐weight deep learning model for cloud applications and remote diagnosis of skin cancers

Abstract

Skin cancer is among the 10 most common cancers. Recent research revealed the superiority of artificial intelligence (AI) over dermatologists to diagnose skin cancer from predesignated and cropped images. However, there remain several uncertainties for AI in diagnosing skin cancers, including lack of testing for consistency, lack of pathological proof or ambiguous comparisons. Hence, to develop a reliable, feasible and user‐friendly platform to facilitate the automatic diagnostic algorithm is important. The aim of this study was to build a light‐weight skin cancer classification model based on deep learning methods for aiding first‐line medical care. The developed model can be deployed on cloud platforms as well as mobile devices for remote diagnostic applications. We reviewed the medical records and clinical images of patients who received a histological diagnosis of basal cell carcinoma, squamous cell carcinoma, melanoma, seborrheic keratosis and melanocytic nevus in 2006–2017 in the Department of Dermatology in Kaohsiung Chang Gung Memorial Hospital (KCGMH). We used the deep learning models to identify skin cancers and benign skin tumors in the manner of binary classification and multi‐class classification in the KCGMH and HAM10000 datasets to construct a skin cancer classification model. The accuracy reached 89.5% for the binary classifications (benign vs malignant) in the KCGMH dataset; the accuracy was 85.8% in the HAM10000 dataset in seven‐class classification and 72.1% in the KCGMH dataset in five‐class classification. Our results demonstrate that our skin cancer classification model based on deep learning methods is a highly promising aid for the clinical diagnosis and early identification of skin cancers and benign tumors.

Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

“…The values of achieved average accuracy for actinic keratoses was 92.3%, basal cell carcinoma was 91.8%, squamous cell carcinoma was 95.1%, melanoma was 94.2%. In 2020, Huang et al [30] had developed a convolution neural network and had achieved an accuracy of 85.8% and More et al [31] had achieved an accuracy of 75.03% on HAM10000 dataset. In future, the accuracy of the proposed model can be improved by applying the noise removal filter in the pre-processing stage.…”

“…They had worked on four pre-trained models such as Inception-v3, ResNet-50, Inception-ResNet-v2 and DenseNet201 and had achieved an accuracy of 81.29%, 81.57%, 81.34% and 73.44% respectively. Huang et al [30] had developed a convolution neural network and had achieved an accuracy of 85.8% and More et al [31] had achieved an accuracy of 75.03% on HAM10000 dataset. In this paper, a model is proposed and pre-trained networks are used for detection of skin disease.…”

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

“…The authors achieved an F2-score sensitivity of 91.7% and 89.5% respectively for skin magnifier with polarized light and advanced dermoscope images. The authors in [ 132 ] used DL techniques to build a skin cancer classification model for binary and multiclass classification of malignant and benign skin tumors. They used Kaohsiung Chang Gung Memorial Hospital and HAM10000 datasets in their study.…”

A Computer‐Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification

“…In the end, they concluded that the training of deep learning models with the best setup of hyperparameters could be performed better than even ensemble models. Hsin et al [ 33 ] presented the automatic lightweight diagnostic algorithm for skin lesion diagnosis. The presented algorithm was more reliable, feasible, and easy to use.…”