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

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Lightweight-CancerNet: a deep learning approach for brain tumor detection.

Asif Raza¹, Muhammad Javed Iqbal¹

¹Department of Computer Science, University of Engineering and Technology Taxila, Taxila, Punjab, Pakistan.

Peerj. Computer Science

|March 10, 2025

Summary

This summary is machine-generated.

A new deep learning model, Lightweight-CancerNet, efficiently detects brain tumors in medical images. This accurate and fast model aids real-time diagnosis, improving patient care and surgical decisions.

Keywords:

Brain tumor detection Efficient diagnosis MobileNet NanoDet Real-time object detection

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Accurate and rapid brain tumor detection is crucial for patient outcomes.
Current deep learning models for medical image analysis are computationally intensive.
There is a need for efficient and accurate deep learning frameworks for brain tumor diagnosis.

Purpose of the Study:

To introduce Lightweight-CancerNet, a novel deep learning architecture for efficient and accurate brain tumor detection.
To address the computational demands of existing deep learning models in medical imaging.
To develop a reliable framework for real-time object detection of brain tumors.

Main Methods:

Developed Lightweight-CancerNet using MobileNet architecture and NanoDet.
Optimized the model for reduced computation time without sacrificing accuracy.
Validated the framework on two magnetic resonance imaging (MRI) datasets, including images with distortions.

Main Results:

Achieved a mean average precision (mAP) of 93.8% and an accuracy of 98%.
Demonstrated significant reduction in computing time, enabling real-time applications.
Confirmed the model's resilience and reliability across diverse MRI data.

Conclusions:

Lightweight-CancerNet offers an efficient and accurate solution for brain tumor detection in medical imaging.
The model's real-time capabilities can enhance clinical decision-making in neurosurgery.
This research contributes to advancing deep learning applications in medical diagnostics.