SKIN CANCER IMAGE DETECTION SYSTEM USING THE CONVOLUTIONAL NEURAL NETWORK MODEL

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Published Mar 1, 2023
https://doi.org/10.31943/teknokom.v6i1.106
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Vol. 6 No. 1 (2023): TEKNOKOM

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Muhamad Suhaedi
Universitas Sultan Ageng Tirtayasa
Hamid Abdillah
Universitas Sultan Ageng Tirtayasa
https://orcid.org/0000-0001-5257-6667
Annisa Rizki Liliandari
Synthesis Academy

Abstract

The development of science and technology (IPTEK) in the current era is growing very rapidly in various fields such as industry, education, especially the health sector. Many technologies can be used, one of which is artificial intelligence technology. This study aims to detect skin cancer images using CNN so that they can be efficient and precise. This research method uses the convolutional neural network (CNN) method, namely image processing, the development of a multilayer perceptron (MLP), in which the neurons of the data are propagated in two dimensions. Because this method has very high accuracy compared to the fuzzy k-nearest neighbors. The results of this study are that there are 7 classes of skin cancer images including actinic keratosis, basal cell carcinoma, dermatofibroma, benign keratosis, melanocytic nevi, vascular lesions and melanoma. From the results of testing the 7 classes using the convolutional neural network (CNN) method with a very high accuracy rate of 99%, 96%, 98%, 99%, 100%, 99% and 96%, respectively. With the conclusion that using the convolutional neural network (CNN) method produces an average accuracy of 98% compared to the Mobilnetv2, Resnet50 and VGG16 models, which means that the CNN model is proven to be more accurate. So it is hoped that this detection system can be applied as a skin cancer detection system for the world of health.

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

How to Cite
Suhaedi, M., Abdillah, H. ., & Rizki Liliandari, A. . (2023). SKIN CANCER IMAGE DETECTION SYSTEM USING THE CONVOLUTIONAL NEURAL NETWORK MODEL. TEKNOKOM, 6(1), 20–28. https://doi.org/10.31943/teknokom.v6i1.106

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