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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Automated Brain Tumor Detection Using Convolutional Neural Network.

Roobal Chaudhary¹, Prawar Chaudhary², Chintan Singh³

¹Department of Forensic Science, Sharda School of Allied Health Sciences, Sharda University, Greater Noida, India.

Biotechnology and Applied Biochemistry

|October 12, 2025

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This summary is machine-generated.

Advanced deep learning models show promise for early brain tumor detection. The U-Net convolutional neural network (CNN) achieved 97.73% accuracy in segmenting tumors, significantly aiding neuro-oncology diagnostics.

Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-oncology

Background:

Accurate brain tumor detection is critical for timely medical intervention.
Conventional methods rely on manual radiological analysis, prone to errors and variability.
Deep learning offers potential to improve diagnostic accuracy and efficiency.

Purpose of the Study:

To evaluate the efficacy of U-Net and Single-Shot Multibox Detector (SSD) deep learning models for early brain tumor detection.
To compare the performance of U-Net for segmentation and SSD for object detection in brain tumor identification.
To assess the potential of these AI techniques in enhancing neuro-oncology diagnostics.

Main Methods:

Utilized U-Net, a convolutional neural network (CNN) renowned for medical image segmentation.

Keywords:

U‐Net brain tumor detection deep learning magnetic resonance imaging (MRI)medical image segmentation single‐shot multibox detector (SSD)

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Employed Single-Shot Multibox Detector (SSD), an established object detection algorithm.

Applied these models to medical scans for brain tumor identification and localization.

Main Results:

The U-Net model demonstrated high performance, achieving 97.73% accuracy in brain tumor segmentation.
The SSD model achieved 58% accuracy, indicating potential as a supplementary tool.
U-Net showed exceptional precision in identifying and localizing brain tumors.

Conclusions:

U-Net is a highly effective method for precise brain tumor detection in medical imaging.
Deep learning, particularly U-Net, significantly improves early detection outcomes in neuro-oncology.
Further research can explore enhancing diagnostic accuracy with these advanced AI techniques.