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All Journal Indonesian Journal of Mathematics and Natural Sciences Journal of Soft Computing Exploration
Izzulhaq, Muhammad Agil
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Other

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Penerapan Algoritma Convolutional Neural Network Arsitektur ResNet50V2 Untuk Mengidentifikasi Penyakit Pneumonia Izzulhaq, Muhammad Agil; Alamsyah, Alamsyah
Indonesian Journal of Mathematics and Natural Sciences Vol. 47 No. 1 (2024): Volume 47 Nomor 1 Tahun 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/p532ny06

Abstract

Pneumonia is a disease that infects the respiratory tract, disrupting the normal function of the human body. Viruses and bacteria are known as common causes of pneumonia. Identification of Pneumonia can use Convolutional Neural Network (CNN). CNN is an effective artificial neural network architecture for image analysis, inspired by how the human brain processes visual information. CNNs are capable of understanding the hierarchical features in images, from lines and angles to complex shapes and objects. This research aims to use ResNet50V2, a popular CNN architecture, to classify X-ray images as either normal or indicative of pneumonia, with the goal of creating an accurate and efficient diagnostic tool. The research method involves using X-ray image datasets for training, validation, and testing, using the ResNet50V2 CNN architecture. The test results show that ResNet50V2 achieves a pneumonia classification accuracy of 93.26%. This study innovatively explores alternative CNN architectures for pneumonia classification, focusing on ResNet50V2.
Tuberculosis classification on chest x-ray images using DenseNet-169 and convolutional block attention module Izzulhaq, Muhammad Agil; Endang Sugiharti
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.14

Abstract

Tuberculosis remains a major global health challenge, and the manual interpretation of chest X-rays is often limited by the subjectivity and shortage of radiology experts. While deep learning approaches like DenseNet have shown promise in medical imaging, the integration of attention mechanisms such as the Convolutional Block Attention Module (CBAM) for tuberculosis detection has been less explored. This study aimed to develop a Convolutional Neural Network (CNN) model utilizing DenseNet-169 combined with CBAM to accurately classify chest X-ray images into normal and tuberculosis classes. A dataset of 7,000 chest X-ray images was preprocessed and partitioned into training, validation, and testing sets. DenseNet-169 served as the backbone architecture, while CBAM was applied to emphasize crucial spatial and channel features. Evaluated across standard metrics, the proposed model achieved an accuracy of 99.43%, a precision of 99.72%, a recall of 99.14%, and an F1-score of 99.43%, successfully outperforming the baseline DenseNet-169 model without CBAM. Ultimately, the integration of CBAM with DenseNet-169 demonstrates remarkable potential in improving tuberculosis detection, confirming that attention mechanisms can substantially enhance deep learning performance in medical imaging.
Co-Authors Alamsyah - Endang Sugiharti