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Updated: Jun 14, 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

Brain tumor detection and segmentation using deep learning.

Rafia Ahsan¹, Iram Shahzadi^2,3, Faisal Najeeb⁴

¹Department of Electrical and Computer Engineering, Medical Image Processing Research Group (MIPRG), COMSATS University Islamabad, Islamabad, Pakistan.

Magma (New York, N.Y.)

|September 4, 2024

Summary

This summary is machine-generated.

This study compares deep learning models for brain tumor detection and segmentation. YOLOv5 combined with 2D U-Net demonstrated superior performance in detecting and segmenting brain tumors accurately.

Keywords:

Brain tumor Classification Deep learning Detection Magnetic resonance imaging (MRI)Segmentation

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Last Updated: Jun 14, 2025

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Published on: November 30, 2022

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-oncology

Background:

Brain tumor detection, classification, and segmentation are complex due to tumor heterogeneity.
Deep learning object detection algorithms' performance on brain tumor data requires further exploration.

Purpose of the Study:

To compare object detection algorithms (Faster R-CNN, YOLO, SSD) for brain tumor detection on MRI data.
To pair the best detection network with 2D U-Net for precise tumor segmentation.

Main Methods:

Evaluation on the Brain Tumor Figshare (BTF) dataset.
Cascading the best object detection network with 2D U-Net for segmentation.
Fine-tuning the detection network on BRATS 2018 data for glioma detection and classification.

Main Results:

YOLOv5 achieved the highest mean Average Precision (mAP) of 89.5% for detecting three tumor types.
The YOLOv5-2D U-Net combination yielded a Dice Similarity Coefficient (DSC) of 88.1% for segmentation, outperforming 2D U-Net alone (80.5%).
The proposed method surpassed Mask R-CNN in both mAP (89.5% vs. 67%) and DSC (88.1% vs. 44.2%).

Conclusions:

A deep learning method combining YOLOv5 and 2D U-Net is proposed for multi-class brain tumor detection, classification, and segmentation.
The proposed method accurately detects various brain tumors and precisely delineates tumor regions.