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Updated: Mar 8, 2026

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

Multimodal brain tumor segmentation and classification based on optimized DeepLabV3 + and fused fire module with

Muhammad Sami Ullah¹, Muhammad Attique Khan², Yunyoung Nam³

¹Department of Computer Science, HITEC University, Taxila, Pakistan.

European Journal of Medical Research

|March 6, 2026

Summary

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We developed SMDeepNet, a novel deep learning model for brain tumor segmentation and classification. This AI achieved high accuracy in segmenting tumors and classifying modalities, outperforming existing methods.

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Neuroscience

Background:

Accurate brain tumor segmentation and classification are crucial for effective treatment planning.
Deep learning models have shown promise but require further optimization for complex medical imaging tasks.

Purpose of the Study:

To propose SMDeepNet, a novel deep learning architecture for enhanced brain tumor segmentation and classification.
To evaluate the performance of SMDeepNet on the BraTS 2023 dataset.

Main Methods:

Developed SMDeepNet, integrating an optimized DeepLabV3+ for segmentation and a Fused Fire Module with Self-Attention for classification.
Employed ResNet-50 backbone with dynamic hyperparameter initialization and Atrous Spatial Pyramid Pooling (ASPP) for feature extraction.

Keywords:

Brain tumor Fire mechanism Magnetic resonance imaging Multimodal Networks fusion Neural network Neuroscience Self-attention Tumor segmentation

Related Experiment Videos

Last Updated: Mar 8, 2026

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

Utilized parallel Fire-Residual Bottleneck (Fire-RB) and Hybrid Efficient Attention (Hybrid-EA) frameworks, incorporating self-attention for classification.

Main Results:

Achieved segmentation accuracy of 0.9871, Dice Score of 0.9420, and Intersection over Union (IOU) of 0.8951.
Attained modality classification accuracy of 0.9920.
Demonstrated superior performance compared to recent state-of-the-art techniques.

Conclusions:

SMDeepNet offers a robust and effective deep learning solution for brain tumor segmentation and classification.
The proposed architecture significantly improves upon existing methods, paving the way for advanced neuro-oncology diagnostics.