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Firdaus, Iqbal
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The Impact of Squeeze-and-Excitation Blocks on CNN Models and Transfer Learning for Pneumonia Classification Using Chest X-ray Images Yunan, Muhammad; Marjuni, Aris; Affandy, Affandy; Soeleman, Mochamad Arief; Firdaus, Iqbal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6693

Abstract

Pneumonia is one of the leading causes of death due to respiratory tract infections, especially in children and the elderly. Early detection using chest X-ray images is crucial to accelerate diagnosis and treatment, but manual interpretation is often subjective and error-prone. This study evaluates the effect of Squeeze-and-Excitation (SE) Block integration on the performance of a custom Convolutional Neural Network (CNN) model and three popular transfer learning architectures: MobileNetV2, VGG16, and InceptionV3 in X-ray image-based pneumonia classification. A dataset of 5,856 images, taken from Chest X-ray Images (Pneumonia) on Kaggle, was processed through preprocessing, undersampling, and augmentation. Each model was tested in two configurations: without and with SE Block. Evaluation was performed using accuracy, precision, recall, F1-score, and test loss metrics. The results show that SE Block integration improves the performance of most models. The accuracy of the custom CNN increased from 95.17% to 95.88%, MobileNetV2 from 97.18% to 97.59%, and VGG16 from 96.88% to 97.69%. InceptionV3 also saw an accuracy increase from 94.06% to 94.16%, although accompanied by an increase in test loss. SE Block proved effective in strengthening the model's emphasis on important features through an inter-channel recalibration mechanism, especially on efficient architectures like MobileNetV2 and complex models like VGG16. These findings support the development of a more accurate, efficient, and adaptive deep learning-based pneumonia diagnosis system, especially for implementation in healthcare facilities with limited resources.
Co-Authors Abdul Kadir Abdurrahman, Ahmad Faruq Affandy Affandy Agnesia, Donna Agung Putra Wijaya, Agung Putra Aprilia, Ayu Boedi, Soelistijono Budi Dermawan Diba Ramadhana Dwi Asmi Eliasta Ketaren, Eliasta Fath, Yusril Al Henny Septiana Amalia Jacob, Louris Marjuni, Aris Melya Riniarti Muhammad Maladi Muhammad Yunan, Muhammad Pertiwi, Salwa Dian Pohan, Sry Dhina Posman Manurung Prihanto, Surya Raffa, Muhammad Rio, Avilla Riswan Ludfi Siska Ayu Widiana Soeleman, Mochamad Arief Sumardi . Suprihatin Suprihatin Supriyanto Wijaya, Mario Eka Zaharani, Rethanita