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All Journal Jurnal TIMES Antivirus : Jurnal Ilmiah Teknik Informatika JURNAL MAHAJANA INFORMASI INFOKUM Jurnal Armada Informatika
Irwan Jani Tarigan
STMIK Methodist Binjai
Computer Science & IT Control & Systems Engineering Library & Information Science

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Journal : INFOKUM

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Oral Lesion Image Classification Using Convolutional Neural Network (CovNets) Method Based on MobileNetV2 Architecture Reza Alamsyah; Irwan Jani Tarigan
INFOKUM Vol. 12 No. 04 (2024): Engineering, Computer and Communication, November 2024
Publisher : Sean Institute

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Abstract

Oral lesions, which can appear in various areas of the oral cavity, are often an early indication of oral cancer, one of the most common cancers worldwide and a leading cause of cancer death in South Asia, Southeast Asia, and the Western Pacific. Oral cancer can affect various parts of the mouth and throat, with contributing factors including genetics, smoking, and viral infections. Early detection is critical for effective management of oral cancer, allowing for early treatment that increases the chance of cure and reduces the risk of complications. This study used a Convolutional Neural Network (CNN) to detect images of oral lesions, including benign and malignant lesions, by utilizing the TensorFlow Object Detection API and data from the Oral Images Dataset. Testing with 40 images (20 benign and 20 malignant lesions) showed an accuracy of 92.5%, a precision of 95%, and a recall of 90%, demonstrating the potential of CNN in efficiently detecting oral lesions.
Co-Authors Alamsyah, Reza Alasi, Tomi Satria Alasi, Tomy Satria Allwine Barus, Andre Armana Putra Cindy Gaol, Delima Lumban Gea, Dandi Sarpinus ginting, Ersada Halim, Marwa Indra Sidabutar Indra Sidabutar Juliana Lorenza, Anggella Nelta Maji Sapdiaz Manullang, Sahat Fernando Marpaung, Adinda Nurrahmah Nasution, Suhendri Nasution, Suhendri Nasution P, Megawati Pandi Barita Nauli Simangunsong Priandy Yaputra Reza Alamsyah Simangunsong, Pandi Barita Nauli Sinurat, Rosmauli Margaret Tongam E Panggabean Wijaya, Vera