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

You might also read

Related Articles

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

Sort by
Same author

An Explainable AI-Based Transfer Learning Method for Breast Cancer Prediction.

Journal of visualized experiments : JoVEĀ·2026
Same author

<b>A new species and a new record of the genus <i>Rhamphothrips</i> Karny (Thysanoptera, Thripidae) from the Western Ghats, India</b>.

ZootaxaĀ·2026
Same author

GC-MS metabolomic profiling and PPARĪ³-targeted <i>in silico</i> approaches for identifying a potential anti-diabetic compound from traditional rice varieties.

Frontiers in nutritionĀ·2026
Same author

An Edge-Enabled Low-Latency Cross-Lingual Speech-to-Text Framework for Efficient Human-Robot Interaction.

Big dataĀ·2026
Same author

An owl-inspired temporal transformer for enhanced shrimp detection in aquatic environments.

Scientific reportsĀ·2026
Same author

Deep learning for early detection of cerebral small vessel disease using self-supervised graph embeddings and retinal image analysis.

Scientific reportsĀ·2026

Related Experiment Video

Updated: Jun 17, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; 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

1.0K

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced

K Vanitha1, Mahesh T R2, S Sathea Sree3

  • 1Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore, India.

BMC Medical Informatics and Decision Making
|August 7, 2024
PubMed
Summary

A new deep learning model combining Xception and MobileNet architectures accurately classifies lung and colon cancers from histopathological images, achieving 99.44% accuracy. This advancement improves diagnostic imaging and aids personalized treatment planning.

Keywords:
Automated cancer classificationClinical AI applicationsColon cancer detectionEnhanced deep learningEnsemble learning modelsGradient-weighted Class Activation Mapping (Grad-CAM)HistopathologyLung cancer diagnosisMedical image analysisXception and MobileNet integration

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

6.8K
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

8.2K

Related Experiment Videos

Last Updated: Jun 17, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; 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

1.0K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

6.8K
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

8.2K

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Lung and colon cancers are leading causes of cancer mortality globally.
  • Accurate histopathological classification via medical imaging is crucial for diagnosis and treatment.
  • Existing diagnostic methods face challenges like overfitting and poor generalizability.

Purpose of the Study:

  • To develop a novel deep learning framework for enhanced classification of lung and colon cancers.
  • To improve feature extraction, model robustness, and reduce overfitting in cancer diagnostics.
  • To integrate interpretability methods for clinical validation and personalized treatment.

Main Methods:

  • A hybrid deep learning model was created by combining Xception and MobileNet architectures.
  • The ensemble model was trained on a comprehensive dataset of histopathological images.
  • Model performance was validated using a balanced test set, incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) for interpretability.

Main Results:

  • The hybrid model achieved an outstanding classification accuracy of 99.44%.
  • The model demonstrated perfect precision and recall for specific cancerous and non-cancerous tissue identification.
  • Grad-CAM visualization provided interpretable insights into the model's diagnostic reasoning.

Conclusions:

  • The developed deep learning framework significantly improves the accuracy and robustness of lung and colon cancer classification from medical images.
  • The model's interpretability through Grad-CAM enhances clinical trust and facilitates personalized treatment strategies.
  • This research paves the way for applying similar advanced AI techniques to diagnose other cancer types.