Automated Ear Infection Detection Deploying Densenet Algorithm

Otoscopy is a diagnostic technique that uses an otoscope to look at the eardrum (tympanic membrane) and external auditory canal. With the help of this portable device, which combines a light source and a magnifying lens or camera, medical practitioners may see the eardrum and canal structures. Otoscopy is frequently used to evaluate the health of the ears and find anomalies including infections, inflammation, wax accumulation, foreign objects, tumours, structural flaws, or indications of trauma. For patients experiencing ear-related symptoms such as discomfort, hearing loss, or discharge, it is a crucial step in the assessment process. Otoscopy is usually done as part of a thorough ear examination, and to further evaluate ear function, it may be combined with additional procedures like audiometry or tympanometry. Although otoscopy is essential for identifying a variety of ear conditions, it is still difficult to accurately and quickly classify otoscopy pictures. In this study, we present a deep learningbased automated otoscopy classification system. Thus, we put together a diverse set of otoscopy pictures that included both healthy anatomy and a range of diseases, such as tumours, infections, inflammations, and structural anomalies. For feature extraction and classification, we used Convolutional Neural Networks (CNNs) using a pre-trained model called the DenseNet framework, a potent family of deep learning models. The model was trained using categorical cross-entropy loss after the dataset had been pre-processed to improve diversity and homogeneity. Common measures including accuracy, precision, recall, F1score, and AUC-ROC were used to assess the model, and hyperparameters were adjusted to maximise performance. Our findings reveal that the suggested method is successful in correctly categorising otoscopy pictures, indicating its potential as a useful clinical tool to support the diagnosis of ear disorders.