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All Journal Jusikom : Jurnal Sistem Komputer Musirawas Sains, Aplikasi, Komputasi dan Teknologi Informasi Journal of Applied Data Sciences International Journal of Advanced Health Science and Technology Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Tri Basuki Kurniawan
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Nursing Public Health

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Progressive Massive Fibrosis Detection Using Generative Adversarial Networks and Long Short-Term Memory Suhendro Y. Irianto; Sri Karnila; M.S. Hasibuan; Deshinta Arrova Dewi; Tri Basuki Kurniawan; Hendra Kurniawan
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.707

Abstract

Contribution: Progressive Massive Fibrosis (PMF) is a severe form of pneumoconiosis, affecting individuals exposed to mineral dust, such as coal miners and workers in the artificial stone industry. This condition causes significant pulmonary impairment and increased mortality. Early and accurate detection is vital for effective management, yet traditional diagnostic methods face challenges in differentiating PMF from other pulmonary diseases due to variability in clinical presentations and limitations in imaging techniques. Idea: The study introduces a novel diagnostic framework that integrates Generative Adversarial Networks (GAN) and Long Short-Term Memory (LSTM) networks to enhance the detection and monitoring of PMF. The GAN generates high-fidelity synthetic imaging data to address the issue of limited datasets, while the LSTM network captures temporal patterns in patient data, enabling real-time monitoring of disease progression. Objective: The primary objective of this research is to develop an AI-driven model that improves the accuracy and efficiency of PMF detection and monitoring, facilitating early diagnosis and better treatment planning. Findings: The integrated GAN-LSTM model significantly outperformed traditional diagnostic methods. It proved high accuracy, a Dice coefficient of 0.85, and an Area Under the Curve (AUC) of 0.92, showing precise differentiation of PMF from other pulmonary conditions, such as lung cancer and tuberculosis. Results: The GAN-LSTM framework achieved an accuracy of 91.3%, suggesting that the fusion of GAN and LSTM technologies can effectively address the challenges of limited datasets and heterogeneous disease progression. The model showed promise in enhancing the non-invasive detection and ongoing monitoring of PMF. Novelty: This research stands for a significant advancement in PMF diagnostics by combining GAN and LSTM technologies in a single framework. This approach improves diagnostic accuracy and eases continuous disease monitoring, offering a non-invasive and highly precise solution for PMF detection.
Integrating Convolutional Neural Networks into Mobile Health: A Study on Lung Disease Detection Muhammad Said Hasibuan; R Rizal Isnanto; Deshinta Arrova Dewi; Joko Triloka; RZ Abdul Aziz; Tri Basuki Kurniawan; Ary Maizary; Anggawidia Wibaselppa
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.660

Abstract

This study presents the development and evaluation of a Convolutional Neural Network (CNN) model for lung disease detection from chest X-ray images, complemented by a mobile application for real-time diagnosis. The CNN model was trained on a diverse dataset comprising images labeled as "NORMAL" and "PNEUMONIA," achieving an overall accuracy of 96%. Compared to traditional machine learning methods such as Support Vector Machine (SVM) and Random Forest, which typically achieve accuracies ranging from 85% to 92%, the proposed CNN model demonstrates superior performance in classifying lung conditions. The model achieved high precision (0.98) and recall (0.96) for pneumonia detection, as well as precision (0.89) and recall (0.95) for normal cases, ensuring both sensitivity and specificity in diagnostic performance. These results indicate that the model minimizes false positives and false negatives, which is crucial for reducing misdiagnoses and improving patient outcomes in clinical settings. To enhance accessibility, an Android-based application was developed, allowing users to upload chest X-ray images and receive instant diagnostic results. The application successfully integrated the trained CNN model, offering a user-friendly interface suitable for healthcare professionals and patients alike. User testing demonstrated reliable performance, facilitating timely and accurate lung disease detection, particularly in areas with limited access to radiologists. These findings highlight the potential of CNNs in medical imaging and the critical role of mobile technology in expanding healthcare accessibility. This innovative approach not only improves diagnostic accuracy but also enables real-time disease detection, ultimately supporting clinical decision-making. Future research will focus on expanding the dataset, incorporating additional lung conditions, and optimizing the model for enhanced robustness in diverse clinical scenarios.
Co-Authors Abdul Muniif Mohd Muhayeddin Adi Wijaya Anggawidia Wibaselppa Ary Maizary Ayu Eka Saputri Aziz, RZ. Abdul Deshinta Arrova Dewi Deshinta Arrova Dewi Deshinta Arrova Dewi Evi Novilia Fadly Fadly Fadly Fatoni Fatoni Harprith Kaur Rajinder Singh Henderi . Hendra Kurniawan Jeyarani Periasamy Joko Triloka M Izman Herdiansyah M Said Hasibuan M. Said Hasibuan M.S. Hasibuan Malathy Batumalay Ming-Lang Yeh Mohd Zaki Zakaria Muhammad Islam Putri Aisha Athira binti Hisham R Rizal Isnanto R Rizal Isnanto Rajermani Thinakaran Shabana Habib Sri Karnila Suhendro Y. Irianto Widya Cholil Yesi Novaria Kunang Yunike, Yunike Yustida Bellini Zena Lusi