%0 Journal Article
%@ 1424-8220
%A Shafi, Imran
%A Sajad, Muhammad
%A Fatima, Anum
%A Gavilanes Aray, Daniel
%A Lipari, Vivian
%A Diez, Isabel de la Torre
%A Ashraf, Imran
%D 2023
%F unic:8655
%J Sensors
%K teeth lesion detection; IoT enabled framework; transfer learning; automated detection model; AlexNet
%N 15
%P 6837
%T Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19
%U http://repositorio.unic.co.ao/id/eprint/8655/
%V 23
%X With a view of the post-COVID-19 world and probable future pandemics, this paper presents an Internet of Things (IoT)-based automated healthcare diagnosis model that employs a mixed approach using data augmentation, transfer learning, and deep learning techniques and does not require physical interaction between the patient and physician. Through a user-friendly graphic user interface and availability of suitable computing power on smart devices, the embedded artificial intelligence allows the proposed model to be effectively used by a layperson without the need for a dental expert by indicating any issues with the teeth and subsequent treatment options. The proposed method involves multiple processes, including data acquisition using IoT devices, data preprocessing, deep learning-based feature extraction, and classification through an unsupervised neural network. The dataset contains multiple periapical X-rays of five different types of lesions obtained through an IoT device mounted within the mouth guard. A pretrained AlexNet, a fast GPU implementation of a convolutional neural network (CNN), is fine-tuned using data augmentation and transfer learning and employed to extract the suitable feature set. The data augmentation avoids overtraining, whereas accuracy is improved by transfer learning. Later, support vector machine (SVM) and the K-nearest neighbors (KNN) classifiers are trained for lesion classification. It was found that the proposed automated model based on the AlexNet extraction mechanism followed by the SVM classifier achieved an accuracy of 98%, showing the effectiveness of the presented approach.