A novel lightweight deep learning model based on SqueezeNet architecture for viral lung disease classification in X-ray and CT images

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2024, Volume and Issue: 10(4)

Published: Oct. 8, 2024

COVID-19 has affected hundreds of millions individuals, seriously harming the global population’s health, welfare, and economy. Furthermore, health facilities are severely overburdened due to record number cases, which makes prompt accurate diagnosis difficult. Automatically identifying infected individuals promptly placing them under special care is a critical step in reducing burden such issues. Convolutional Neural Networks (CNN) other machine learning techniques can be utilized address this demand. Many existing Deep models, albeit producing intended outcomes, were developed using parameters, making unsuitable for use on devices with constrained resources. Motivated by fact, novel lightweight deep model based Efficient Channel Attention (ECA) module SqueezeNet architecture, work identify patients from chest X-ray CT images initial phases disease. After proposed was tested different datasets two, three four classes, results show its better performance over models. The outcomes shown that, comparison current heavyweight our models reduced cost memory requirements computing resources dramatically, while still achieving comparable performance. These support notion that help diagnose Covid-19 being easily implemented low-resource low-processing devices.

Language: Английский

An Efficient Deep Learning Framework using CapsNet and SOM for Multidrug-Resistant Tuberculosis Detection and Analysis in CXR Images

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 258, P. 3251 - 3263

Published: Jan. 1, 2025

Language: Английский

Enhancing Pulmonary Diagnosis in Chest X-rays through Generative AI Techniques

J — Multidisciplinary Scientific Journal, Journal Year: 2024, Volume and Issue: 7(3), P. 302 - 318

Published: Aug. 13, 2024

Chest X-ray imaging is an essential tool in the diagnostic procedure for pulmonary conditions, providing healthcare professionals with capability to immediately and accurately determine lung anomalies. This modality fundamental assessing confirming presence of various issues, allowing timely effective medical intervention. In response widespread prevalence infections globally, there a growing imperative adopt automated systems that leverage deep learning (DL) algorithms. These are particularly adept at handling large radiological datasets high precision. study introduces advanced identification model utilizes VGG16 architecture, specifically adapted identifying anomalies such as opacity, COVID-19 pneumonia, normal appearance lungs, viral pneumonia. Furthermore, we address issue generalizability, which prime significance our work. We employed data augmentation technique through CycleGAN, which, experimental outcomes, has proven enhancing robustness model. The combined performance VGG CycleGAN demonstrates remarkable outcomes several evaluation metrics, including recall, F1-score, accuracy, precision, area under curve (AUC). results showcased achieving 98.58%. contributes advancing generative artificial intelligence (AI) analysis establishes solid foundation ongoing developments computer vision technologies within sector.

Language: Английский