Android skin cancer detection and classification based on MobileNet v2 model

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Android skin cancer detection and classification based on MobileNet v2 model
 
Creator Wibowo, Adi
Adhi Hartanto, Cahyo
Wisnu Wirawan, Panji
 
Subject Deep learning; Skin cancer; Android; MobileNet v2
 
Description The latest developments in the smartphone-based skin cancer diagnosis application allow simple ways for portable melanoma risk assessment and diagnosis for early skin cancer detection. Due to the trade-off problem (time complexity and error rate) on using a smartphone to run a machine learning algorithm for image analysis, most of the skin cancer diagnosis apps execute the image analysis on the server. In this study, we investigate the performance of skin cancer images detection and classification on android devices using the MobileNet v2 deep learning model. We compare the performance of several aspects; object detection and classification method, computer and android based image analysis, image acquisition method, and setting parameter. Skin cancer actinic Keratosis and Melanoma are used to test the performance of the proposed method. Accuracy, sensitivity, specificity, and running time of the testing methods are used for the measurement. Based on the experiment results, the best parameter for the MobileNet v2 model on android using images from the smartphone camera produces 95% accuracy for object detection and 70% accuracy for classification. The performance of the android app for object detection and classification model was feasible for the skin cancer analysis. Android-based image analysis remains within the threshold of computing time that denotes convenience for the user and has the same performance accuracy with the computer for the high-quality images. These findings motivated the development of disease detection processing on android using a smartphone camera, which aims to achieve real-time detection and classification with high accuracy.
 
Publisher Universitas Ahmad Dahlan
 
Contributor Department of Informatics, Faculty of Sciences and Mathematics, Diponegoro University, Indonesia
 
Date 2020-07-12
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/492
10.26555/ijain.v6i2.492
 
Source International Journal of Advances in Intelligent Informatics; Vol 6, No 2 (2020): July 2020; 135-148
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/492/ijain_v6i2_p135-148
 
Rights https://creativecommons.org/licenses/by-sa/4.0
 

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