Setiawan, Nico Bernando and Natalia, Friska and Ferdinand, Ferry Vincenttius and Sudirman, Sud and Ko, Chang Seong (2021) Classification of Skin Diseases and Disorders using Convolutional Neural Network on a Mobile Application. ICIC Express Letters, 12 (8). pp. 715-721. ISSN 2185-2766
Full text not available from this repository.Abstract
Skin diseases and disorders are common, yet underestimated, in Indonesia. More serious types of them are often left untreated for a long period of time because of the lack of information regarding the therapeutic process of its treatment and available medical support. In this study, we use a deep learning approach using deep convolutional neural network to classify different types of skin diseases and disorders, namely psoriasis, ringworm, and eczema. The algorithm is implemented as an Android-based mobile application app because of the pervasive use of the Android platform in Indonesia. The app development uses the TensorFlow library for its low-level implementation of deep learning and the Android Studio IDE for high-level processes. In addition, the application also provides information on how to treat any skin diseases, it also provides a list of hospitals in the district and the city where the user resides if they wish to get medical treatment immediately. The artefact of this research is available on the PlayStore for people to download and use.
| Item Type: | Article |
|---|---|
| Keywords: | Skin diseases and disorders; Machine Learning; Deep Convolutional Neural Network; TensorFlow; Android Studio |
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods 600 Technology (Applied Sciences) > 610 Medicine and Health > 616 Diseases |
| Divisions: | Faculty of Engineering & Informatics > Information System |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 24 Nov 2021 14:57 |
| Last Modified: | 24 Nov 2021 14:57 |
| URI: | https://kc.umn.ac.id/id/eprint/19265 |
Actions (login required)
 |
View Item |
