Oral Lesion Image Classification Using Convolutional Neural Network (CovNets) Method Based on MobileNetV2 Architecture - Reza Alamsyah; Irwan Jani Tarigan (INFOKUM, 2024)
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.