Earticle

Biometric has been implemented on numerous public facilities to enhance the security system. Fingerprint and face are the most popular biometric. Emerging technology has introduced potential biometric such as palm print, lips, teeth, vein and ear. However, most of this biometrics requires a special device to capture it. Thus, the implementation of such system will be costly. Iannarelli [1, 2] has proved that ear biometric is having a great potential for identifying a person. In this research work, an attempt is made to improve the detection and finally to segment the human ear from the whole image of human’s head. The success of this stage is very important for achieving the later goal, such as recognition and classification. This paper introduces a novel method for ear segmentation. Proposed method is based on morphological analysis fused with RBF neural network. Experiment shows that the proposed method has delivered a promising result.

Objectives. Otitis media is a common infection worldwide. Owing to the limited number of ear specialists and rapid devel-opment of telemedicine, several trials have been conducted to develop novel diagnostic strategies to improve the di-agnostic accuracy and screening of patients with otologic diseases based on abnormal otoscopic findings. Althoughthese strategies have demonstrated high diagnostic accuracy for the tympanic membrane (TM), the insufficient ex-plainability of these techniques limits their deployment in clinical practice. Methods. We used a deep convolutional neural network (CNN) model based on the segmentation of a normal TM into fivesubstructures (malleus, umbo, cone of light, pars flaccida, and annulus) to identify abnormalities in otoscopic ear im-ages. The mask R-CNN algorithm learned the labeled images. Subsequently, we evaluated the diagnostic performanceof combinations of the five substructures using a three-layer fully connected neural network to determine whetherear disease was present. Results. We obtained the receiver operating characteristic (ROC) curve of the optimal conditions for the presence or ab-sence of eardrum diseases according to each substructure separately or combinations of substructures. The highestarea under the curve (0.911) was found for a combination of the malleus, cone of light, and umbo, compared withthe corresponding areas under the curve of 0.737?0.873 for each substructure. Thus, an algorithm using these fiveimportant normal anatomical structures could prove to be explainable and effective in screening abnormal TMs. Conclusion. This automated algorithm can improve diagnostic accuracy by discriminating between normal and abnormalTMs and can facilitate appropriate and timely referral consultations to improve patients’ quality of life in the contextof primary care.