KLASIFIKASI CITRA DERMOSKOPI KANKER KULIT DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN)

PRATAMA, ALDI ANUGRA and Fachrurrozi, Muhammad and Rizqie, M. Qurhanul (2022) KLASIFIKASI CITRA DERMOSKOPI KANKER KULIT DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

A Dermoscopy image is one way to visually diagnose skin cancer. Dermoscopy allows for the visualization of subsurface skin structures in the epidermis, these structures are usually not visible to the naked eye. This makes the Demorscopy image very suitable to be a source of classification data. In this research, the software is built to classify skin cancer using the Convolutional Neural Network method. The Convolutional Neural Network (CNN) architectural model created in this research will be compared with the 3 other CNN models that have been previously available, Mobile Net, ResNet50 V2, and VGG16. The model trained using the HAM10000 dataset which contains 7 types of skin cancer, with 27.385 training data, 6847 validation data dan 8559 test data. The Mobile Net model has the highest performance at 95% accuracy, 95% sensitivity, 95% f1-score, 96% precision, and 99% specificity, followed by ResNet50 V2 which has 93% accuracy , 93% precision, 93% sensitivity, 93% f1-score ,and 99% specificity, then VGG16 which has 92% accuracy, 92% sensitivity, 92% f1-score, 93% precision, and 99% specificity, and finally the CNN model which has 87% accuracy, 87% precision, 87% sensitivity, 87% f1-score ,and 98% specificity.

Item Type:	Thesis (Undergraduate)
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Aldi Anugra Pratama
Date Deposited:	28 Jul 2022 08:39
Last Modified:	28 Jul 2022 08:39
URI:	http://repository.unsri.ac.id/id/eprint/75120

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