Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 5, 2025Journal of education for pure science.Open Access

liver diseases detection and classification using deep learning algorithms

View Full Paper
Ask AI
Bookmark
Share

Authors

ATAhmed ThijeelKAKadhim H. Alibraheem

Discussion

Loading...

Member takes

Overview

This analysis demonstrates improved liver disease classification accuracy using deep learning techniques, highlighting the role of image segmentation.

Key Points

  • Achieving a classification accuracy of 97.94% using the ResNet50 model indicates significant advancements in liver disease detection.
  • With advanced neural networks, the proposed study shows the importance of preprocessing and feature extraction for accurate liver disease classification.
  • Utilizing convolutional neural networks and image segmentation techniques enhances diagnostic capabilities for liver disease detection.
  • The findings suggest the potential for deep learning models to assist medical professionals in diagnosing liver diseases effectively.

Cite This Study

Thijeel et al. (2025) studied this question.

synapsesocial.com/papers/68bb4df56d6d5674bcd021fehttps://doi.org/10.32792/jeps.v15i3.698
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Liver cancer detection using Artificial Intelligence2024 · 1 citations
  2. 2A Comprehensive Liver Tumor Detection and Stages Classification Using Deep Learning and Image Processing Techniques2024 · 5 citations
  3. 3Customized m-RCNN and hybrid deep classifier for liver cancer segmentation and classification2024 · 9 citations
  4. 4Leveraging Segmentation and Classification Techniques for Liver cancer Prediction in deep learning2024 · 1 citations
  5. 5A comprehensive review on deep learning for liver cancer detection: research challenges and future directions2026