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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
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.
Enhancing Diagnostic Accuracy of Polycystic Ovary Syndrome Classification in Ultrasound Images Using a Hybrid Deep Learning Model of VGG16 and AlexNet Maisarah, Hj.; Soeleman, M. Arief; Pujiono, Pujiono; Firdaus, Iqbal; Firdaus, Gusti Aditya Aromatica
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.4932

Abstract

Diagnosis of Polycystic Ovary Syndrome (PCOS) using ultrasound (USG) imaging still faces a major challenge in the form of inter-observer variability, which can lead to inconsistent diagnostic outcomes and increase the risk of misclassification. This limitation highlights the urgent need for an automated artificial intelligence (AI)–based system capable of performing ultrasound image classification with greater objectivity, accuracy, and consistency. This study aims to develop an automated PCOS classification model based on a hybrid Convolutional Neural Network (CNN) architecture that integrates VGG16 and AlexNet through a feature concatenation mechanism, following preprocessing and data augmentation steps to enhance model generalization. The model’s performance was evaluated using accuracy, precision, recall, F1-score, and specificity as key metrics. Experimental results demonstrate that the VGG16–AlexNet hybrid model achieved the best performance, with an accuracy of 98.26%, precision of 97.90%, recall of 97.90%, F1-score of 97.90%, and specificity of 98.52%. These results outperform other hybrid configurations such as VGG16–MobileNetV2, VGG16–ResNet50, and VGG16–InceptionV3, each of which achieved accuracies above 96%. These findings confirm that combining the feature depth of VGG16 with the computational efficiency of AlexNet enables more comprehensive extraction of spatial and textural patterns in ultrasound images. Consequently, the proposed hybrid model offers a promising AI-driven diagnostic support system that not only enhances the accuracy of PCOS detection but also assists clinicians in making faster, more objective, and consistent medical decisions.
Co-Authors Abdul Kadir Abdurrahman, Ahmad Faruq Affandy Affandy Agnesia, Donna Agung Putra Wijaya Aprilia, Ayu Boedi, Soelistijono Budi Dermawan Diba Ramadhana Dwi Asmi Eliasta Ketaren, Eliasta Fath, Yusril Al Firdaus, Gusti Aditya Aromatica Henny Septiana Amalia Jacob, Louris Maisarah, Hj. Marjuni, Aris Melya Riniarti Muhammad Maladi Muhammad Yunan, Muhammad Pertiwi, Salwa Dian Pohan, Sry Dhina Posman Manurung Prihanto, Surya Pujiono Pujiono Raffa, Muhammad Rio, Avilla Riswan Ludfi Siska Ayu Widiana Soeleman, M. Arief Soeleman, Mochamad Arief Sumardi . Suprihatin Suprihatin Supriyanto Wijaya, Mario Eka Zaharani, Rethanita