LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images

Abstract

In recent years, lung disease has increased manyfold, causing millions of casualties annually. To combat the crisis, an efficient, reliable, and affordable lung disease diagnosis technique has become indispensable. In this study, a multiclass classification of lung disease from frontal chest X-ray imaging using a fine-tuned CNN model is proposed. The classification is conducted on 10 disease classes of the lungs, namely COVID-19, Effusion, Tuberculosis, Pneumonia, Lung Opacity, Mass, Nodule, Pneumothorax, and Pulmonary Fibrosis, along with the Normal class. The dataset is a collective dataset gathered from multiple sources. After pre-processing and balancing the dataset with eight augmentation techniques, a total of 80,000 X-ray images were fed to the model for classification purposes. Initially, eight pre-trained CNN models, AlexNet, GoogLeNet, InceptionV3, MobileNetV2, VGG16, ResNet 50, DenseNet121, and EfficientNetB7, were employed on the dataset. Among these, the VGG16 achieved the highest accuracy at 92.95%. To further improve the classification accuracy, LungNet22 was constructed upon the primary structure of the VGG16 model. An ablation study was used in the work to determine the different hyper-parameters. Using the Adam Optimizer, the proposed model achieved a commendable accuracy of 98.89%. To verify the performance of the model, several performance matrices, including the ROC curve and the AUC values, were computed as well.

Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis

“…Among the 63 studies, 56 studies identified pneumothorax on chest radiography [ 26 – 81 ], four studies on computed tomography [ 82 – 85 ], one study on ECG [ 86 ], one study used chest radiography and photography using a smartphone [ 87 ], and one study used chest radiography and tabular data [ 88 ]. Six studies developed and internally tuned DLs [ 37 , 52 , 63 , 67 , 74 , 76 ], 25 studies also internally tested their DLs [ 32 , 33 , 35 , 38 , 40 , 41 , 43 , 45 , 47 , 48 , 50 , 55 , 60 , 65 , 69 , 70 , 73 , 75 , 79 – 83 , 85 , 86 ] and 32 studies externally tested the DLs [ 26 – 31 , 34 , 36 , 39 , 42 , 44 , 46 , 49 , 51 , 53 , 54 , 56 – 59 , 61 , 62 , 64 , 66 , 68 , 71 , 72 , 77 , 78 , 84 , 87 , 88 ].…”

“…As for model development, to generate a reference standard for image labelling, 18 studies used expert consensus [ 27 – 33 , 35 – 38 , 49 , 53 – 55 , 71 , 77 , 83 ], two relied on the opinion of a single expert reader [ 76 , 85 ], 16 used pre-existing radiological reports or other imaging modalities [ 34 , 41 , 43 , 45 , 46 , 52 , 60 , 61 , 67 , 75 , 78 – 82 , 87 ], one study defined their reference standard as surgical confirmation (indicated for surgery) [ 86 ], 11 studies used mixed methods (any combination of the aforementioned) [ 40 , 47 , 48 , 50 , 51 , 62 , 63 , 65 , 69 , 70 , 73 ] and two studies did not report how their reference standard was generated [ 74 , 88 ]. As for model testing, to generate a reference standard for image labelling, 26 studies used expert consensus [ 26 – 28 , 30 – 33 , 38 , 39 , 44 , 51 , 54 – 57 , 61 , 64 , 66 , 68 , 71 – 73 , 77 , 80 , 83 , 84 ], two relied on the opinion of a single expert reader [ 58 , 85 ], 11 used pre-exist...…”

“…The proportion of participants with pneumothorax in each dataset also ranged widely (median (IQR) 17.2% (10.8–25.0%)). 23 studies did not describe the sex of the study participants [ 27 , 31 – 33 , 36 – 38 , 55 , 59 , 62 , 65 , 69 – 71 , 73 , 76 – 78 , 81 , 82 , 86 – 88 ] and 24 studies did not include age information [ 27 , 31 – 33 , 36 – 38 , 55 , 59 , 62 , 65 , 69 – 71 , 73 , 74 , 76 – 78 , 81 , 82 , 86 – 88 ]. Detailed dataset characteristics are shown in supplementary table S2 .…”

“…The size of the training (median (IQR) 17 265 (8540–86 524)), tuning (median (IQR) 1598 (924–3468)) and test (median (IQR) 1684 (575–3107)) datasets at the patient level varied widely ( table 1 ). Two out of 50 (4%) studies that developed a model did not report the size of each dataset separately [ 40 , 69 ]. In studies that performed external model validation, the median dataset size was 1137 (range 175–112 120).…”

Three-Dimensional Postoperative Results Prediction for Orthognathic Surgery through Deep Learning-Based Alignment Network

“…Deep learning has developed rapidly in recent years, making it possible to automatically extract information in the medical field from diagnoses using medical imaging and pattern analysis [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Deep neural networks (DNNs), a type of deep learning, have been widely applied to medical images because of their high performance in detection, classification, and segmentation [ 16 , 17 , 18 , 19 , 20 ].…”