A CNN-Based Chest Infection Diagnostic Model: A Multistage Multiclass Isolated and Developed Transfer Learning Framework

Abstract

In 2019, a deadly coronaviral infection (COVID-19) that infected millions of people globally was detected in China. This fatal virus affects the respiratory system and currently spreads to more than 200 nations worldwide. COVID-19 may be found using a chest X-ray scan, a reliable imaging method. Although an expert may examine an X-ray scan manually, this process takes a lot of time. Therefore, deep convolutional neural networks (CNNs) may be utilized to automate this procedure. In this work, at the first step, a novel isolated 19-layer CNN model is developed from scratch to detect chest infections using X-rays. Then, the developed model is reutilized to distinguish the type of chest infection, such as COVID-19, fibrosis, pneumonia, and tuberculosis, using the transfer learning approach. Stochastic gradient descent with momentum is utilized to optimize the model. The proposed multistage framework shows 98.85% and 97% classification accuracies for chest infection detection (binary classification between normal and patient) and four-class subclassification (COVID-19, fibrosis, pneumonia, and tuberculosis) for an online chest X-ray dataset. The reliability of the proposed multistage CNN model was further validated through a new dataset, showing an accuracy of 98.5%. The proposed multistage methodology took minimal training time compared to publically available pretrained models. Therefore, the presented multistage deep learning framework can help doctors in clinical practices.

Low CRB-65 Scores Effectively Rule out Adverse Clinical Outcomes in COVID-19 Irrespective of Chest Radiographic Abnormalities

“…The effect of secondary bacterial infections and therapies instigated for COVID-19 could not be assessed. In future research, integrating artificial intelligence methods such as deep convolutional neural networks (CNN) [ 32 , 33 ] with CRB-65 might offer value in prognosticating COVID-19 patients. Finally, this study was conducted at a time before routine vaccination and availability of many contemporary therapies for COVID-19.…”

Pneumonia Detection Technique Empowered with Transfer Learning Approach

“…Their research clarifies the effectiveness of transfer learning in this field. Similar to this, Ali et al (2023) presented a CNN-based model designed especially for the diagnosis of chest infections, making use of an isolated, multistage, multiclass transfer learning architecture. This method shows how versatile CNNs are for problems involving medical imaging.…”

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework

“…To improve relevance, it is critical to remove noise and undesirable regions. The cropping approach is used to estimate extreme points, while noise-reduction techniques such as erosion and dilatation are used to reduce undesirable elements [19,33]. The data augmentation was also applied to adjust the size (to 227 × 227) and balance the dataset (1000 samples per class) using rotation and translation.…”

“…Furthermore, the color, size, and other features of skin cancer types are very similar. Image processing and machine vision use for various medical imaging applications has grown tremendously in the past decade [17][18][19][20][21][22]. Using these strategies speeds up the diagnosis process and reduces human error.…”