Download Project Document/Synopsis
Dental caries is the most common oral health condition. Some studies have shown that only 21% of people go to dental clinics and hospitals for dental examinations. The rate might be significantly lower in low- and middle-income countries where dental examinations are expensive and not covered by insurance.
Contrary to the accessible routine checkup, smartphones can be available and affordable in most countries. Thus, a smartphone-based diagnostic tool, which most of the population can easily access, could be a game changer in increasing the number of examinations of people with dental caries.
Deep learning, with a major model – Convolutional Neural Networks (CNNs), uses network structures consisting of multiple layers for automatically learning and self-learning backpropagation. Our python-based Dental Caries Detection System easily detects dental caries from an image uploaded by the user.
Deep learning with image input has been explosively growing and promising to become an important platform in medical images. One of its most popular applications in the medical field is classification. Applications of deep learning in dentistry are remarkable in a variety of fields such as teeth-related diseases, dental plaque, and periodontium.
Considering the anomalies in the existing system computerization of the whole activity is being suggested after the initial analysis. In this system, the user will need to upload an image from their device’s gallery. Once the user uploads an image, the system will proceed with detection by classifying if there are any caries present or not.
The front-end involves Html, CSS, and JavaScript and the back-end involves Python. The framework used is Django and the database is MySQL. The model used here is CNN and the dataset is extracted from Kaggle (consisting of two classes of carries and no carries).
Advantages
- It is user-friendly.
- The system can easily detect dental caries.
- The user will no longer need to take an appointment for a dental caries examination.