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Related Experiment Video

Updated: Jul 25, 2025

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

Multi-modal medical image classification using deep residual network and genetic algorithm.

Muhammad Haris Abid¹, Rehan Ashraf¹, Toqeer Mahmood¹

¹Department of Computer Science, National Textile University, Faisalabad, Pakistan.

|June 29, 2023

Summary

This summary is machine-generated.

Artificial intelligence (AI) in healthcare significantly improves medical image classification. A deep learning model, ResNet50, achieved 98.61% accuracy, enhancing diagnostic capabilities.

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Area of Science:

Medical Imaging
Artificial Intelligence
Deep Learning

Background:

Accurate medical image classification is crucial for diagnosis and treatment planning.
Conventional methods struggle with the semantic gap, requiring manual feature extraction.
Deep learning, particularly Convolutional Neural Networks (CNNs), shows promise in overcoming these limitations.

Purpose of the Study:

To bridge the semantic gap in medical image classification.
To enhance classification performance for multi-modal medical images using deep learning.
To evaluate the efficacy of the ResNet50 model for this task.

Main Methods:

Utilized a deep learning-based model, ResNet50.
Trained and validated the model on a dataset of 28,378 multi-modal medical images.
Evaluated performance using accuracy, precision, recall, and F1-score.

Main Results:

The proposed ResNet50 model achieved an overall accuracy of 98.61%.
The model demonstrated superior classification performance compared to other state-of-the-art methods.
Key evaluation parameters (accuracy, precision, recall, F1-score) confirmed the model's effectiveness.

Conclusions:

Deep learning, specifically the ResNet50 model, effectively bridges the semantic gap in medical image classification.
The developed model offers a significant advancement in accurate and consistent diagnostic decision-making.
This research directly benefits healthcare services through improved diagnostic tools.