A novel computer-aided diagnosis system utilizing a vision transformer feature extractor and k-nearest neighbors algorithm achieved 99.00% accuracy in classifying ear diseases from otoscope images.
Does a vision transformer-based feature extractor combined with machine learning classifiers accurately classify ear diseases from otoscope images?
A novel deep learning model using vision transformers and kNN achieved 99.00% accuracy in classifying ear diseases from otoscope images, offering a potential decision support tool for ENT specialists.
Abstract Ear diseases are defined as pathological conditions that indicate dysfunction or abnormal function of the ear organ, which is part of the auditory system of living organisms that regulates hearing and balance functions. These diseases usually manifest as conditions that affect the internal components of the ear structure and can manifest themselves with symptoms such as hearing loss, ear pain, balance problems, and fluid accumulation in the ear. The accuracy of the diagnosis depends on expert knowledge and subjective opinion. This method is prone to human error. This study presents a novel computer-aided diagnosis system for otoscope images of ear diseases, utilizing a vision transformer-based feature extractor combined with machine learning classifiers to provide accurate second opinions for ENT specialists. For this purpose, a new model based on state-of-the-art vision transformer feature extractor and machine learning models is proposed. In the experimental study, the dataset, comprising 880 eardrum images categorized into four classes (CSOM, earwax, myringosclerosis, and normal), was split into training (70%), validation (10%), and testing (20%) subsets. Each image was preprocessed to 420 × 380 pixels to fit the input dimensions of the models. The vision transformer architecture was utilized for feature extraction, followed by classification using various machine learning algorithms including kNN, SVM, and random forest. As a result, the model using vision transformer feature extractor and k-nearest neighbors (kNN) algorithm achieved 99.00% accuracy. In this study, a deep learning-based and computer-aided diagnosis system, in other words, a computational model, was developed instead of the current human error-prone disease diagnosis method used by ear nose throat (ENT) specialists. The main purpose of the deep learning-based decision support system is to support the diagnosis process where expert knowledge is difficult to access and to provide an alternative opinion to the expert diagnosis.
Demircan et al. (Tue,) conducted a other in Ear diseases (CSOM, earwax, myringosclerosis, normal) (n=180). Vision transformer feature extractor with k-nearest neighbors (kNN) classifier vs. Traditional CNN-based models was evaluated on Classification accuracy. A novel computer-aided diagnosis system utilizing a vision transformer feature extractor and k-nearest neighbors algorithm achieved 99.00% accuracy in classifying ear diseases from otoscope images.