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Updated: Feb 2, 2026

Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images.

Mostefa Ben Naceur¹, Rachida Saouli², Mohamed Akil³

¹Smart Computer Sciences Laboratory, Department of Computer Sciences, University of Biskra, Biskra, Algeria; Gaspard Monge Computer Science Laboratory, ESIEE-Paris, University Paris-Est Marne-la-Vallée, France.

Computer Methods and Programs in Biomedicine

|November 13, 2018

Summary

This summary is machine-generated.

This study introduces novel deep learning models for efficient brain tumor segmentation in MRI scans. The models achieve state-of-the-art results, aiding in faster clinical diagnosis and treatment planning.

Keywords:

Brain tumor segmentation Convolutional neural networks Deep learning Fully automatic Hyper-parameters Training

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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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Published on: June 9, 2018

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Efficient brain tumor segmentation in multi-sequence MR images is crucial for timely clinical diagnosis and treatment.
Glioblastomas present challenges due to their varied sizes, shapes, contrasts, and locations within the brain.

Purpose of the Study:

To develop novel deep learning models for the automatic segmentation of brain tumors, specifically Glioblastomas.
To improve the efficiency and accuracy of brain tumor segmentation compared to existing methods.

Main Methods:

Proposed three end-to-end Incremental Deep Convolutional Neural Networks (CNNs) models.
Employed Ensemble Learning for enhanced model efficiency.
Introduced a new training strategy to optimize hyper-parameters and accelerate training.

Main Results:

Achieved state-of-the-art performance on the BRATS-2017 dataset without post-processing.
Attained an average Dice score of 0.88 for complete brain tumor region segmentation.
Demonstrated rapid segmentation with an average processing time of 20.87 seconds using GPU implementation.

Conclusions:

The developed deep learning models are effective for accurate brain tumor segmentation.
These models can significantly reduce diagnostic time for physicians.
The proposed approach offers a promising tool for neuro-oncology.