Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

You might also read

Related Articles

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

Sort by

Same author

Deep Belief VGG-16 Hybrid Model for Brain Tumor Classification Using MRI Images.

NMR in biomedicine·2025

Same author

NT-proBNP in Cardioembolic Stroke.

Neurology India·2023

Same author

Smart IoT in Breast Cancer Detection Using Optimal Deep Learning.

Journal of digital imaging·2023

Same author

FWLICM-Deep Learning: Fuzzy Weighted Local Information C-Means Clustering-Based Lung Lobe Segmentation with Deep Learning for COVID-19 Detection.

Journal of digital imaging·2022

Same author

Jaya Ant lion optimization-driven Deep recurrent neural network for cancer classification using gene expression data.

Medical & biological engineering & computing·2021

Same author

Computer-Aided Diagnosis System for Alzheimer's Disease Using Positron Emission Tomography Images.

Interdisciplinary sciences, computational life sciences·2021

Search research articles

Related Experiment Video

Updated: Jul 21, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

ExpHBA Deep-IoT: Exponential Honey Badger Optimized Deep Learning For Breast Cancer Detection in IoT Healthcare

R Rajeswari¹, G V Sriramakrishnan², Ch Vidyadhari³

¹Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India. rajimaniphd@gmail.com.

Journal of Digital Imaging

|July 25, 2023

Summary

This study introduces an Exponential Honey Badger Optimization-based Deep Convolutional Neural Network (EHBO-based DCNN) for early breast cancer detection. The novel method significantly improves accuracy, aiding in timely diagnosis and treatment.

Keywords:

Deep Covolutional Neural Network Exponential Weighted Moving Average Honey Badger Optimization Local Gabor Binary Pattern Local Optimal Oriented Pattern

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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

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

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jul 21, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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

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

Published on: December 15, 2023

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Oncology

Background:

Breast cancer (BC) is a leading global health concern for women.
Early detection of BC is crucial for reducing mortality and improving treatment outcomes.

Purpose of the Study:

To develop an advanced deep learning model for the early identification of breast cancer.
To leverage the Internet of Things (IoT) for efficient medical data processing and classification.

Main Methods:

A hybrid Exponential Honey Badger Optimization (EHBO) algorithm, combining Honey Badger Optimization (HBO) and Exponential Weighted Moving Average (EWMA), was developed.
EHBO was used for selecting optimal cluster heads to transfer medical data to a base station for BC categorization.
Statistical and texture features were extracted, followed by data augmentation and classification using a Deep Convolutional Neural Network (DCNN).

Main Results:

The EHBO-based DCNN achieved high performance with testing accuracy, sensitivity, and specificity of 0.9051, 0.8971, and 0.9029, respectively.
The proposed method demonstrated superior accuracy compared to Multi-Layer Perceptron (MLP), deep learning, Support Vector Machine (SVM), and ensemble-based classifiers.

Conclusions:

The EHBO-based DCNN offers a promising approach for accurate and early breast cancer detection.
The integration of EHBO with DCNN in an IoT framework enhances diagnostic capabilities for breast cancer.