Development of Skin Disease Discrimination System Based on Deep Learning Using Lesions Segmented by Brightness and Color Information

2021 ◽  
Vol 31 (2) ◽  
pp. 109-116
Author(s):  
Yong-Hyun Cho ◽  
Sung-Won Youn
Keyword(s):  
Deep Learning ◽  
Skin Disease ◽  
Color Information ◽  
Disease Discrimination

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

scholarly journals Skin disease diagnosis with deep learning: A review

2021 ◽  
Vol 464 ◽  
pp. 364-393
Author(s):  
Hongfeng Li ◽  
Yini Pan ◽  
Jie Zhao ◽  
Li Zhang
Keyword(s):  
Deep Learning ◽  
Skin Disease ◽  
Disease Diagnosis

scholarly journals Deep Learning in Skin Disease Image Recognition: A Review

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 208264-208280
Author(s):  
Ling-Fang Li ◽  
Xu Wang ◽  
Wei-Jian Hu ◽  
Neal N. Xiong ◽  
Yong-Xing Du ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Skin Disease

scholarly journals Identification of Skin Disease Using Deep Learning

2020 ◽  
pp. 1011-1116
Author(s):  
Shravani Kharat ◽  
Pooja Shinde ◽  
Preeti Malwadkar ◽  
Dipti Chaudhari
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Skin Disease ◽  
Skin Diseases ◽  
Disease Prediction ◽  
Learning Technology ◽  
Network Approach ◽  
Neural Network Approach ◽  
Prevention And Early Detection ◽  
Common Skin

Globally, skin diseases are among the most common health problems in all humans irrespective of age. Prevention and early detection of these diseases can improve the chance of surviving. This model illustrates the identification of skin diseases providing more objective and reliable solutions using deep learning technology and convolutional neural network approach. In this model, the system design, implementation and identification of common skin diseases such as acne, blister, eczema, warts etc. are explained. The system applies deep learning technology to train itself with various images of skin diseases from the Kaggle platform. The accuracy obtained by using deep learning is 83.23%. The main objective of this system is to achieve maximum accuracy of skin disease prediction. Moreover, if the disease is identified the system provides detailed information about the diseases along with home remedies.

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