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

Chromatographic Methods: Classification01:12

Chromatographic Methods: Classification

3.9K
Chromatographic techniques are classified in three ways: the classification is based on the physical state of the stationary and mobile phases, how the mobile phase and the stationary phase contact each other, or through the chemical or physical processes that isolate the components of the sample. Typically, the mobile phase is either a liquid or gas, while the stationary phase is either a solid or a liquid layer applied to a solid surface.
Chromatographic techniques are typically named by...
3.9K
Methods of Classification and Identification01:28

Methods of Classification and Identification

1.2K
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
1.2K
The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

56.1K
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
56.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.9K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

101.8K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
101.8K
Renal Drug Clearance: Comparison Between Renal Excretion Methods01:08

Renal Drug Clearance: Comparison Between Renal Excretion Methods

618
Renal clearance is a critical parameter encompassing kidney filtration, secretion, and reabsorption processes. It is calculated using a specific equation to determine the rate at which the kidneys clear a drug.
Renal clearance is often associated with the renal glomerular filtration rate (GFR), which represents the rate at which plasma is filtered through the glomeruli in the kidney. When drug reabsorption is minimal and there is no active secretion, renal clearance is closely related to the...
618

You might also read

Related Articles

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

Sort by
Same author

Secoisolariciresinol diglucoside ameliorates muscarinic acetylcholine receptor mediated activation of NLRP3 inflammasome in cardiomyocytes.

Molecular biology reports·2026
Same author

Dapagliflozin confers protection against ferroptosis in cardiomyocyte via the inhibition of interferon-gamma pathway.

Molecular biology reports·2026
Same author

Landscape factors influencing the distribution of rare submerged plant species: an environmental DNA (eDNA) study.

PeerJ·2026
Same author

Whole-brain connectome analysis for elucidating specific structural neural networks in idiopathic normal-pressure hydrocephalus.

Magma (New York, N.Y.)·2026
Same author

Hip-angle dependent changes in the shear modulus of biceps femoris short head with knee extension.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·2026
Same author

MRI-based cerebrospinal fluid volumetric indices for predicting tap test response in idiopathic normal pressure hydrocephalus.

Radiological physics and technology·2026

Related Experiment Video

Updated: Feb 5, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

742

Comparison of medical image classification accuracy among three machine learning methods.

Tomoko Maruyama1, Norio Hayashi1, Yusuke Sato2

  • 1Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan.

Journal of X-Ray Science and Technology
|September 19, 2018
PubMed
Summary

Convolution Neural Networks (CNN) maintain accuracy in medical image classification, unlike Support Vector Machines (SVM) and Artificial Neural Networks (ANN), which are sensitive to image quality degradation from DICOM to JPEG formats.

Keywords:
CNNDICOMDeep learningJPEG

More Related Videos

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

509
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.8K

Related Experiment Videos

Last Updated: Feb 5, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

742
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

509
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.8K

Area of Science:

  • Medical imaging analysis
  • Machine learning in healthcare
  • Computer-aided diagnosis

Background:

  • Low-quality medical images can significantly impact the precision of machine learning algorithms.
  • Accurate image classification is fundamental for effective clinical image interpretation.

Purpose of the Study:

  • To evaluate and compare the accuracy of different machine learning methods for medical image classification.
  • To investigate the effect of image quality on classification performance.

Main Methods:

  • Three machine learning techniques were employed: Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolution Neural Network (CNN).
  • A unified dataset was created using two file formats, DICOM (Digital Imaging and Communications in Medicine) and JPEG (Joint Photographic Experts Group), to assess performance under varying image quality.

Main Results:

  • Convolution Neural Network (CNN) demonstrated consistent accuracy across both DICOM and JPEG datasets.
  • Support Vector Machine (SVM) and Artificial Neural Network (ANN) showed a decline in classification accuracy when transitioning from the higher-fidelity DICOM format to the compressed JPEG format.

Conclusions:

  • Convolution Neural Network (CNN) outperforms traditional machine learning methods that rely on manual feature extraction in medical image classification tasks.
  • CNNs offer superior robustness against image quality variations compared to SVM and ANN.