In humans, the disease occurrence is happens in several ways and to manage it, it is necessary to implement appropriate diagnosis and treatment. Maintaining the oral health is a prime task and any abnormality will lead to various other health issues. This work considered the tooth enamel caries for the examination, which needs early diagnosis and treatment. This research proposed deep-learning scheme with EfficientNet (EN) model based examination of the tooth enamel caries. When a digital photograph of the tooth region is available, it is easy for evaluation the severity. This research presented a work to identify he mild and harsh enamel caries from the tooth based on the digital images. The different phases of this work includes the following sections; tooth-image collection and modifying its dimension, deep- features extraction with En-model, Softmax-based classification and identification of best two DL-model, reducing its features to 50% and serially integrating these values to generate a fused-feature vector, and verifying the merit using the machine-learning classifiers and 3-fold cross validation. The result of this work confirms that the developed system works well on the image database and provides>98% with the considered image examination task.
Manic et al. (Thu,) studied this question.