SEGMENTASI LESI KULIT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

CHAYANTI, DEWI and Fachrurrozi, M. and Firdaus, Firdaus (2021) SEGMENTASI LESI KULIT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

Skin lesions are the first clinical symptoms of diseases like chickenpox, melanoma and others. With digital image processing for skin cancer detection, it is possible to make a diagnosis without any physical contact with the skin. Factors such as residue (hair and ruler markers), unclear borders, variable contrast, differences in shape and color differences in dermoscopy images of skin lesions make automatic analysis quite difficult. The presence of hair on the skin lesions can be removed effectively using segmentation. Dermoscopy image segmentation has been researched and developed in many literatures using various methods. In this study, a skin lesion segmentation system was developed using the Convolutional Neural Network (CNN) method with the U-Net architecture which produced 6 results models from parameter tuning. The best model has the highest evaluation results with Pixel Accuracy, Intersection over Union (IoU), and F1 Score of 95.89%, 90.37% and 92.54%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Lesi kulit, melanoma, convolutional neural network, U-Net
Subjects:	T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 12934 not found.
Date Deposited:	05 Jul 2021 07:16
Last Modified:	05 Jul 2021 07:16
URI:	http://repository.unsri.ac.id/id/eprint/49189

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