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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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NAMSTCD: A Novel Augmented Model for Spinal Cord Segmentation and Tumor Classification Using Deep Nets.

Ricky Mohanty¹, Sarah Allabun², Sandeep Singh Solanki³

¹School of Information System, ASBM University, Bhubaneswar 754012, Odisha, India.

Diagnostics (Basel, Switzerland)

|May 16, 2023

Summary

This study introduces an augmented deep learning model for spinal cord segmentation and tumor classification. The novel approach enhances accuracy and speed, improving scalability for clinical applications.

Keywords:

classification convolutional cord mask regions neural network segmentation segments spinal tumor

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Spinal cord segmentation is crucial for diagnosing and treating spinal conditions.
Existing models often lack scalability, focusing on specific spinal regions.
This limits their effectiveness in comprehensive spinal cord analysis.

Purpose of the Study:

To develop a novel augmented deep learning model for accurate spinal cord segmentation and tumor classification.
To overcome the limitations of existing models by addressing the entire spinal cord.
To improve the scalability and clinical applicability of spinal cord analysis tools.

Main Methods:

A novel augmented model was developed using deep neural networks.
The model segments all five spinal cord regions, creating separate, expert-tagged datasets.
Multiple Mask Regional Convolutional Neural Networks (MRCNNs) and specialized CNNs (VGGNet 19, YoLo V2, ResNet 101, GoogLeNet) were employed for segmentation and classification.

Main Results:

The proposed model achieved a 14.5% improvement in segmentation efficiency.
It demonstrated 98.9% accuracy in tumor classification.
A 15.6% increase in speed performance was observed compared to state-of-the-art models.

Conclusions:

The augmented model offers superior performance in spinal cord segmentation and tumor classification.
Its high accuracy, efficiency, and speed make it suitable for clinical deployment.
The model's scalability across different tumor types and spinal regions enhances its utility in diverse clinical scenarios.