Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Signals01:30

Classification of Signals

342
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
342
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

145
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
145
Force Classification01:22

Force Classification

1.0K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.0K
Classification of Systems-II01:31

Classification of Systems-II

125
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
125
Classification of Systems-I01:26

Classification of Systems-I

161
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
161
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Single-model deep learning approach for simultaneous cervical vertebral maturation staging and skeletal jaw relationship on lateral cephalograms using YOLOv8 and CNN.

BMC oral health·2026
Same author

Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques.

Scientific reports·2026
Same author

Interpretable Machine Learning-Based Concentric Regional Analysis of OCTA Images for Enhanced Diabetic Retinopathy Detection.

Bioengineering (Basel, Switzerland)·2026
Same author

The impact of anatomical variations of chorda tympani nerve on taste outcome after endoscopic stapedotomy.

Acta otorrinolaringologica espanola·2026
Same author

Role of hyaluronic acid gel as an adjunct to temporalis fascia graft tympanoplasty in total tympanic membrane perforation: A randomized controlled trial.

Acta otorrinolaringologica espanola·2026
Same author

Facial nerve outcomes following vestibular schwannoma surgery: Multivariate analysis of possible prognostic factors.

Acta otorrinolaringologica espanola·2026

Related Experiment Video

Updated: May 14, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

913

Optimized fine-tuned ensemble classifier using Bayesian optimization for the detection of ear diseases.

Israa Elmorsy1, Waleed Moneir2, Ahmed I Saleh3

  • 1Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.

Computers in Biology and Medicine
|April 11, 2025
PubMed
Summary

A novel deep learning model accurately diagnoses ear conditions from otoscopic images, improving accuracy and reducing misdiagnosis rates for external and middle ear diseases. This automated system aids in timely treatment, preventing hearing loss.

Keywords:
Bayesian optimizationConvolutional neural networkEnsemble modelFine-tuningHyperparametersImage classificationsOtoscopic imagesTympanic membrane

More Related Videos

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.1K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

Related Experiment Videos

Last Updated: May 14, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

913
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.1K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Otolaryngology

Background:

  • External and middle ear diseases are prevalent, particularly in children.
  • Delayed diagnosis and treatment of ear conditions can lead to hearing loss.
  • Current diagnostic methods rely on otolaryngologist expertise, which can be subjective and error-prone.

Purpose of the Study:

  • To develop a deep learning-based automated system for diagnosing ear conditions.
  • To improve the accuracy and specificity of ear disease classification.
  • To create a tool that assists in the early detection of ear pathologies.

Main Methods:

  • A weighted average voting ensemble classifier was developed using MobileNet and DenseNet169.
  • Bayesian optimization was used for hyperparameter tuning.
  • The model was fine-tuned on a public dataset of 282 otoscopic images, excluding the Tympanostomy Tubes class.

Main Results:

  • The proposed model achieved 99.54% accuracy and an Area Under the Curve (AUC) of 1.
  • Grad-CAM++ saliency maps were used to visualize important features in otoscopic images.
  • The ensemble approach enhanced classification ability for ear disease detection.

Conclusions:

  • The developed deep learning model shows significant promise for automated ear disease classification.
  • The system can improve diagnostic accuracy and reduce misdiagnosis rates.
  • This automated tool has the potential to aid clinicians in diagnosing ear conditions more effectively.