ADConv-Net: Advanced Deep Convolution Neural Network for COVID-19 Diagnostics Using Chest X-Ray and CT Images

• Published in: SN computer science Vol. 6; no. 5; p. 423
• Main Authors: Kumar, Sunil, Bhowmik, Biswajit
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.06.2025; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Automation; Classification; Computed tomography; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Convolution; Coronaviruses; COVID-19; Data Structures and Information Theory; Datasets; Decision making; Diagnosis; Epidemics; Image classification; Infections; Information Systems and Communication Service; Lung cancer; Machine learning; Medical electronics; Medical imaging; Neural networks; Original Research; Pattern Recognition and Graphics; Performance evaluation; Performance measurement; Pneumonia; Research Advancements in Intelligent Computing; Respiratory diseases; Severe acute respiratory syndrome; Software Engineering/Programming and Operating Systems; Viral infections; Viruses; Vision; X-rays; COVID-19; CT Images; Artificial Intelligence; Deep Learning; X-Ray Images; Classification
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-025-03923-4

The worldwide COVID-19 epidemic has emerged as a significant concern, affecting daily lives and underscoring the importance of early diagnosis for effective treatment in medical and healthcare settings. Current diagnostic testing for COVID-19 is sluggish, typically requiring hours to get results. Detection of COVID-19 from medical imaging presents a challenging task that has gained substantial interest from experts worldwide. Essential imaging modalities for diagnosing COVID-19 include chest X-rays and computed tomography (CT) scans. By contrast, most of the chest radiography can be completed in within fifteen minutes. Thus, employing chest radiography gives a possibility for early and reliable diagnosis of COVID-19, intending to relieve therapeutic obstacles for patients and speed up the diagnostic process. Recently, deep learning (DL) techniques have been shown to be effective in image-based diagnostics. This paper proposed an advanced deep convolution neural network (ADConv-Net) for COVID-19 detection and categorization using chest X-ray and CT images. The proposed technique is not only capable of recognizing critical connections and similarities in image classification, but also leads to improved diagnostic accuracy. The proposed model undergoes thorough evaluation for standard performance metrics. After evaluation, the ADConv-Net model achieves high accuracies of 98.84% and 97.25% in training and testing for X-ray images and 99.41% and 98.87% in training and testing for CT images, respectively. Additionally, the proposed model demonstrates strong performance, with AUC values of 0.993 and 0.996 for X-ray and CT images, respectively. Further, the model also introduces a heatmap approach for displaying COVID-19 disease areas. Subsequently, radiologists can find COVID-19 disorders in chest X-ray and CT images with this approach.