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Journals / Türk Doğa ve Fen Dergisi / 2026 / Cilt: 13 Sayı: 1

Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network

Hüseyin Fırat(Dicle Üniversitesi)0000-0002-1257-8518
Hüseyin Üzen(BİNGÖL ÜNİVERSİTESİ)0000-0002-0998-2130
Pages
54–61
Publication Date
2024-03-26
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results demonstrated the model's exceptional performance, achieving an accuracy of 98.81%, precision of 98.79%, recall rate of 98.24%, and F1-score of 98.51%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Pneumonia is a global health concern, responsible for a significant number of deaths.
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Key Findings & Data:Its diagnostic challenge arises from visual similarities it shares with various respiratory diseases, such as tuberculosis, complicating accurate identification.
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Conclusion & Impact:Results demonstrated the model's exceptional performance, achieving an accuracy of 98.81%, precision of 98.79%, recall rate of 98.24%, and F1-score of 98.51%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Deep learning · Chest X-Ray images · Pneumonia detection

Abstract

Pneumonia is a global health concern, responsible for a significant number of deaths. Its diagnostic challenge arises from visual similarities it shares with various respiratory diseases, such as tuberculosis, complicating accurate identification. Furthermore, the variability in acquiring and processing chest X-ray (CXR) images can impact image quality, posing a hurdle for dependable algorithm development. To address this, resilient data-centric algorithms, trained on comprehensive datasets and validated through diverse imaging methods and radiology expertise, are imperative. This study presents a deep learning approach designed to distinguish between normal and pneumonia cases. The model, a hybrid of MobileNetV2 and the Squeeze-and-Excitation (SE) block, aims to reduce learnable parameters while enhancing feature extraction and classification. Integration of the SE block enhances classification performance, despite a slight parameter increase. The model was trained and tested on a dataset of 5856 CXR images from Kaggle's medical imaging challenge. Results demonstrated the model's exceptional performance, achieving an accuracy of 98.81%, precision of 98.79%, recall rate of 98.24%, and F1-score of 98.51%. Comparative analysis with various Convolutional neural network-based pre-trained models and recent literature studies confirmed its superiority, solidifying its potential as a robust tool for pneumonia detection, thus addressing a critical healthcare need.

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Cite This Article

Fırat, H., Üzen, H. (2024). Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network. Türk Doğa ve Fen Dergisi, Cilt: 13 Sayı: 1, 54–61. https://doi.org/10.46810/tdfd.1363218

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