Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

Abstract

Background and Motivation: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes—including COVID-19—are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. Method: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks—namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152—for classification of up to five classes of pneumonia. Results: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p < 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes <1 s while demonstrating reliability and stability. Conclusions: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

“…Multilabel classification is not new [ 21 , 124 , 128 , 129 ]. For multilabel classification, the models are trained with multiple classes, for example, if there are two or more than two classes, then the gold standard must consist of two or more than two classes [ 124 , 129 ].…”

“…It is challenging to distinguish COVID-19 pneumonia from interstitial pneumonia or other lung illnesses; as a result, manual classification can be skewed based on radiological expert opinion. As a result, an automated computer-aided diagnostics (CAD) system is sorely needed to categorize and characterize the condition [ 21 ], as it delivers excellent performance due to minimal inter-and intra-observer variability.…”

“…With the advancements of artificial intelligence (AI) technology [ 22 , 23 , 24 ], machine learning (ML) and deep learning (DL) approaches have become increasingly popular for detection of pneumonia and its categorization. There have been several innovations in ML and DL frameworks, some of which are applied to lung parenchyma segmentation [ 25 , 26 , 27 ], pneumonia classification [ 21 , 25 , 28 ], symptomatic vs. asymptomatic carotid plaque classification [ 29 , 30 , 31 , 32 , 33 ], coronary disease risk stratification [ 34 ], cardiovascular/stroke risk stratification [ 35 ], classification of Wilson disease vs. controls [ 36 ], classification of eye diseases [ 37 ], and cancer classification in thyroid [ 38 ], liver [ 39 ], ovaries [ 40 ], prostate [ 41 ], and skin [ 42 , 43 , 44 ].…”

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans

“…Khan et al [ 86 ] applied the EfficientNetB network for the classification into four classes and achieved an accuracy of 96.13%. Our previous work [ 69 ] used 3611 COVID-19 and 13,833 other images to classify them into two, three, and five classes. We applied VGG16, NASNetMobile, and DenseNet201 models and achieved an accuracy of 99.84%, 96.63%, and 92.70%, with an AUC of 1.0, 0.97, and 0.92 for two, three, and five-class classifications, respectively.…”

“…Five different matrices were utilized for the performance evaluation, namely: accuracy, precision, recall, F1-score, and area under the curve (AUC). The mathematical equations for each matrix are given in the equation below [ 28 , 60 , 68 , 69 ]: where TP: True Positive, TN: True Negative, FP: False Positive, and FN: False Negative.…”