Pengembangan Aplikasi berbasis Android untuk Mengenali Jenis Lesi Kulit Menggunakan Convolutional Neural Network - Sengkey, Blessynta Christesa; Paseru, Debby; Pandelaki, Steven (Jurnal Pekommas, 2024)
Skin lesions are skin abnormalities or disorders in the form of changes, damage, abnormal growth of the skin, such as changes in texture, color, appearance of lumps and spots on the skin. This disease certainly disrupts people's activities and behavior every day because of the reactions it causes, such as sensations of itching, pain, stinging and excessive heat. However, knowledge of the types of skin lesions by the lay public is still lacking and a system is needed that can provide information regarding primary skin lesions. Image processing as part of machine learning can recognize types of primary skin lesions through applications that use Convolutional Neural Network (CNN). This method can perform good feature extraction and classification, so it is very suitable for image detection. Research was carried out on 4 classes of lesions, namely macular, urticarial, popular and vesicular. Based on the test results with the CNN model, it was found that the average accuracy value was 95% with the calculation of values in the macular class with precision 91%, recall 100%, f-1 score 95%, urticaria class with precision 100%, recall 91%, f-1 score 95%, papule class with precision 98%, recall 93%, f-1 score 96% and vesicular class with precision 93%, recall 99%, f-1 score 96%.