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LesionScanNet: dual-path convolutional neural network for acute appendicitis diagnosis.

Muhab Hariri¹, Ahmet Aydın², Osman Sıbıç³

¹Electrical and Electronics Engineering Department, Çukurova University, 01330 Adana, Turkey.

Health Information Science and Systems

|December 10, 2024

Summary

A new AI model, LesionScanNet, accurately detects acute appendicitis from CT scans. This lightweight deep learning approach achieves 99% accuracy, outperforming existing methods for medical image analysis.

Keywords:

Acute appendicitis CNN Deep learning Disease detection Medical image classification

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer-Aided Diagnosis

Background:

Acute appendicitis presents with abdominal pain, vomiting, and fever.
Computed tomography (CT) aids diagnosis but faces challenges with anatomical variations.
Accurate and efficient appendicitis detection remains crucial in clinical practice.

Purpose of the Study:

To introduce LesionScanNet, a novel Convolutional Neural Network (CNN) model.
To develop a computer-aided detection (CAD) system for acute appendicitis using CT images.
To evaluate the performance and generalizability of the proposed model.

Main Methods:

A dataset of 2400 CT images was utilized.
LesionScanNet, a lightweight CNN with DualKernel blocks (3x3 and 1x1 filters), was developed.
The model's performance was benchmarked against other deep learning models.

Main Results:

LesionScanNet achieved a 99% accuracy score on the test dataset.
The model demonstrated superior performance compared to benchmark deep learning models.
The model showed generalization capabilities on chest X-ray datasets for pneumonia and COVID-19 detection.

Conclusions:

LesionScanNet is a lightweight and robust deep learning network for medical image analysis.
The model achieves superior performance with a reduced number of parameters.
Its application can be extended to diverse medical imaging domains beyond appendicitis detection.