Improving Chest X-ray Disease Detection via Lightweight and Efficient Transfer Learning Models

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 07 | Jul 2025

p-ISSN: 2395-0072

www.irjet.net

Improving Chest X-ray Disease Detection via Lightweight and Efficient Transfer Learning Models Muskan Agrawal*, Prof. Neha Khare** *Research Scholar Department of CSE, Takshshila Institute of Engineering & Technology, Jabalpur, M.P. **Prof., Department of CSE, Takshshila Institute of Engineering & Technology, Jabalpur, M.P. ---------------------------------------------------------------------------***-------------------------------------------------------------------------Abstract- This paper presents an enhanced deep learning efficient ventilation. Many diseases such as asthma,

Lung inconsistencies represent an increased risk of mortality and morbidity in the world population. This risk results in increased contamination due to the lack of efficient ventilation solutions in many factories and the lack of

bronchitis, and pertussis and covid-19 share cough as a common condition. Cough sounds are usually unique to all respiratory illnesses, allowing doctors to diagnose the disease from the cough itself. Therefore, many solutions in digital technology using big data analytics, the internet of things (iot), block chain, and artificial intelligence (ai) have used machine learning (ml), deep learning (dl), and more. It was proposed to identify diseases from coughing voice [1]. Furthermore, healthcare systems are more involved in ai, helping doctors predict and diagnose a variety of diseases [2], particularly in the past year, when the covid 19 virus has become a pandemic and providing appropriate services. [3] For patients who did not have enough hospitals. Because of the fatal consequences of respiratory diseases, it is important to develop inexpensive and comfortable techniques to control them. According to the world health organization (who), health technology has shown a major contribution to improving the treatment of several respiratory diseases. Furthermore, ai is the most promising technique that changes the embodiment of diagnosis and disease detection when appropriate use occurs [4]. 5] Many studies on diagnostic and control tools diagnosed for diseases diagnosed from 55 works have been examined. They said cost-effective devices such as mobile apps, text messaging/sms and portable technology prove their potential in diagnosing a variety of respiratory diseases. Amrulloh et al. [6] we investigated ai techniques used to identify asthma diseases. Research has discussed the most frequently used ai methods for recognizing asthma, and the most frequently used techniques are Ann (artificial neural network), dt (decision tree), and rf (random forest). Similarly, anand et al. [7] examined the latest technology used to beat covid-19 on various scales. The study discovered ways that this technique can help physicians recognize covid areas of infection, imaging, and recognition of best drug therapy, based on patient data analysis. Furthermore, bales et al. [8] Checks four levels of covid-19 to reduce the number of transportation used by blocking, reduce the number of tourism industry in contrast to food and food, and reduce the number of transportation used. I discovered the impact. The telecommunications industry has seen an increase in the

© 2025, IRJET

ISO 9001:2008 Certified Journal

approach for the automated classification of chest X-ray images into multiple disease categories, including COVID-19, pneumonia, and normal cases. The work expands upon existing binary classification models (pneumonia vs. normal) by introducing a more comprehensive three-class classification system using an enriched dataset and a wider range of convolutional neural network (CNN) architectures. The existing model was trained on a limited dataset of 5,863 images with only two categories. In contrast, the proposed system utilizes a significantly larger dataset comprising 15,153 images across three clinically important classes. A variety of state-of-the-art CNN models were evaluated, including Xception, VGG16, ResNet50, EfficientNetB0, EfficientNetV2L, VGG19, DenseNet121, MobileNetV2, and InceptionResNetV2. The models were trained using carefully tuned data augmentation strategies to improve generalization and performance. Results show that the proposed models significantly outperform the existing implementations in terms of AUC (Area under the Curve) scores. Notably, the InceptionResNetV2 and MobileNetV2 architectures achieved AUC scores of 99.39% and 99.26%, respectively, compared to 82% and NA in earlier work. The improved performance highlights the impact of a more diverse dataset, better augmentation strategies, and inclusion of more recent model architectures. This research demonstrates the potential of deep learning to support clinical decision-making in respiratory disease detection, especially in pandemic scenarios like COVID-19. Keywords: Deep Learning, Convolutional Neural Networks (CNNs), Chest X-ray Classification, COVID-19 Detection, Pneumonia Detection, Transfer Learning, Image Augmentation, Multiclass Classification.

I. INTRODUCTION

|

Impact Factor value: 8.315

|

|

Page 673