Pneumonia and tuberculosis are both serious and life-threatening diseases, with pneumonia, caused by infections that inflame the air sacs in the lungs, and tuberculosis, caused by the bacterium Mycobacterium tuberculosis, affecting the lungs but can also spread to other parts of the body.
Objective
A deep learning (DL) approach to accurately diagnose and perform multi-class classification of lung diseases, namely pneumonia and tuberculosis, has been designed and implemented using chest computed tomography (CT) slices in this work.
Methods
The DenseNet-121 model is utilised to select the significant slices from the input dataset based on the provided labelled data. These selected slices have been augmented, and the augmented CT slices are further processed to utilise the U-Net model for segmenting the lung and lesion regions. The tuberculosis and pneumonia lesions considered in this work were cavitation, consolidation and ground-glass opacities (GGO). These lesions have been elucidated by an expert radiologist. A novel model, termed I-DINet (Improved DenseNet-121 and InceptionNet-V3), has been developed for feature extraction, and the extracted features have been used to train a densely connected layer within the network to perform classification. In this proposed I-DINet, selected layers from DenseNet-121 and Inception V3 networks have been retained, while others have been excluded. Furthermore, hyperparameter tuning has been conducted using a genetic algorithm (GA) to optimise the model's performance.
Results
The I-DINet model demonstrated superior results in terms of 98.76% accuracy, 98.87% recall, 99.38% specificity, 98.72% precision, and 98.79% F1 score when evaluated on a diverse public dataset compiled from various open-source platforms, including Kaggle, Mendeley, IEEE DataPort, and Radiopaedia. The I-DINet model has also been validated using open-source datasets from Kaggle (Montgomery and Shenzhen datasets) and a real-time dataset.
Conclusion
I-DINet outperforms standalone DenseNet-121 and Inception-V3 models, helping doctors make quicker and more precise diagnoses, which would ultimately result in improved patient outcomes.
MargaratGSHemalathaGMishraA, et al.Early diagnosis of tuberculosis using deep learning approach for IoT based healthcare applications. Comput Intell Neurosci2022; 1: 1–9.
2.
BetshrineRRNehemiahKHMarishanjunathCS, et al.Diagnosis of pulmonary edema and COVID-19 from CT slices using squirrel search algorithm, support vector machine and back propagation neural network. J Intell Fuzzy Syst2023; 44: 5633–5646.
3.
IsaacANehemiahHKKannanA. Computer-Aided diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms. Comput Biol Med2020; 124: 103940.
4.
WangLZhouHXuN, et al.A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images. iScience2023; 26: 107005. doi: https://doi.org/10.1016/j.isci.2023.107005
5.
OuyangXHuoJXiaL, et al.Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans Med Imaging2020; 39: 2595–2605.
6.
ZhangHTZhangJSZhangHH, et al.Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. Eur J Nucl Med Mol Imaging2020; 47: 2525–2532.
7.
MahmoudiRBenameurNMabroukR, et al.A deep learning-based diagnosis system for COVID-19 detection and pneumonia screening using CT imaging. Appl Sci2022; 12: 4825.
8.
HaqIMazharTNasirQ, et al.Machine vision approach for diagnosing tuberculosis (TB) based on computerized tomography (CT) scan images. Symmetry (Basel)2022; 14: 1997.
9.
WangGLiuXLiC, et al.A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images. IEEE Trans Med Imaging2020; 39: 2653–2663.
10.
QianXFuHShiW, et al.M3Lung-Sys: a deep learning system for multi-class lung pneumonia screening from CT imaging. IEEE J Biomed Health Inform2020; 24: 3539–3550.
11.
Oloko-ObaMViririS. Ensemble of EfficientNets for the diagnosis of Tuberculosis. Comput Intell Neurosci2021; 2021: 9790894.
12.
PuttaguntaMKRaviS. Detection of Tuberculosis based on deep learning based methods. J Phys Conf Ser2021; 1767: 012004.
13.
IsaacANehemiahHKDunstonSD, et al.Feature selection using competitive coevolution of bio-inspired algorithms for the diagnosis of pulmonary emphysema. Biomed Signal Process Control2022; 72: 103340.
14.
SweetlinJDNehemiahHKKannanA. Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images. Comput Methods Programs Biomed2017; 145: 115–125.
15.
DhaliaSweetlinJNehemiahHKKannanA. Computer aided diagnosis of pulmonary hamartoma from CT scan images using ant colony optimization based feature selection. Alexandria Eng J2018; 57: 1557–1567.
16.
Elgin ChristoVRKhanna NehemiahHMinuB, et al.Correlation-based ensemble feature selection using bioinspired algorithms and classification using backpropagation neural network. Comput Math Methods Med2019; 2019: 7398307.
17.
ElizabethDSNehemiahHKRajCSR, et al.A novel segmentation approach for improving diagnostic accuracy of cad systems for detecting lung cancer from chest computed tomography images. J Data Inf Qual2012; 3: 1–16.
18.
Oloko-ObaMViririS. Diagnosing tuberculosis using deep convolutional neural network. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Proceedings, volume 9, 2020, pp.151-161. Springer International Publishing.
19.
ChristeAPetersAADrakopoulosD, et al.Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images. Invest Radiol2019; 54: 627–632.
20.
LeeTAhnSYKimJ, et al.Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs. Eur. Radiol2024; 34: 4206–4217.
21.
YanCWangLLinJ, et al.A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis. Eur Radiol2022; 32: –2.
22.
TangSWangCNieJ, et al.EDL-COVID: ensemble deep learning for COVID-19 case detection from chest X-ray images. IEEE Trans Ind Informatics2021; 17: 6539–6549.
23.
ChenYLinYXuX, et al.Classification of lungs infected COVID-19 images based on inception-ResNet. Comput Methods Programs Biomed2022; 225: 107053.
24.
ShiHLuJZhouQ. A Novel Data Augmentation Method Using Style-Based GAN for Robust Pulmonary Nodule Segmentation. In: Proceedings of 32nd Chinese Control and Decision Conference CCDC 2020, 2020, pp.2486–2491. IEEE.
25.
TustisonNJAvantsBBLinZ, et al.Convolutional neural networks with template-based data augmentation for functional lung image quantification. Acad Radiol2019; 26: 412–423.
26.
GoceriE. Medical image data augmentation: techniques, comparisons and interpretations. Artif Intell Rev2023; 56: 12561–12605.
27.
SweetlinJDNehemiahHKKannanA. Computer aided diagnosis of drug sensitive pulmonary tuberculosis with cavities, consolidations and nodular manifestations on lung CT images. Int J Bio-Inspired Comput2019; 13: 71–85.
28.
SweetlinJDNehemiahHKKannanA. Computer aided diagnosis of pulmonary hamartoma from CT scan images using ant colony optimization based feature selection. Alexandria Eng J2018; 57: 1557–1567.
29.
WuJQianT. A survey of pulmonary nodule detection, segmentation and classification in computed tomography with deep learning techniques. J Med Artif Intell2019; 2: 8. doi: 10.21037/jmai.2019.04.01
30.
NajeebSBhuiyanMIH. Spatial feature fusion in 3D convolutional autoencoders for lung tumour segmentation from 3D CT images. Biomed Signal Process Control2022; 78: 103996.
31.
JibinsinghBRHarichandranKNJayakannanK, et al.Diagnosis of COVID-19 from computed tomography slices using flower pollination algorithm, k-nearest neighbor, and support vector machine classifiers. Artif Intell Health2025; 2: 14-28.
32.
UsmanMLeeBDByonSS, et al.Volumetric lung nodule segmentation using adaptive ROI with multi-view residual learning. Sci Rep2020; 10: 1–15.
33.
FerdinandusFXSantosoJSetiawanEI, et al.3-D Visualization for lung COVID-19 infection based on U-Net CNN segmentation. IEEE Trans Instrum Meas2023; 72: 1–11.
34.
GhaderzadehMAsadiFJafariR, et al.Deep convolutional neural network-based computer-aided detection system for COVID-19 using multiple lung scans: design and implementation study. J Med Internet Res2021; 23: e27468.
35.
AlshmraniGMMNiQJiangR, et al.A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images. Alexandria Eng J2023; 64: 923–935.
36.
HussainEHasanMRahmanMA, et al.Corodet: a deep learning based classification for COVID-19 detection using chest X-ray images. Chaos Solitons Fractals2021; 142: 110495.
37.
ChenYLinYXuX, et al.Classification of lungs infected COVID-19 images based on inception-ResNet. Comput Methods Programs Biomed2022; 225: 107053.
38.
KalantarRHindochaSHunterB, et al.Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (cycle-GAN) for radiomics and deep learning in the era of COVID-19. Sci Rep2023; 13: 1–15.
39.
HussainAAminSULeeH, et al.An automated chest X-ray image analysis for COVID-19 and pneumonia diagnosis using deep ensemble strategy. IEEE Access2023; 11: 97207–97220.
40.
YaselianiMHamadaniAZMaghsoodiAI, et al.Pneumonia detection proposing a hybrid deep convolutional neural network based on two parallel visual geometry group architectures and machine learning classifiers. IEEE Access2022; 10: 62110–62128.
41.
TekerekAAl-RaweIAM. A novel approach for prediction of lung disease using chest X-ray images based on DenseNet and MobileNet. Wirel Pers Commun2023: 1–15.
42.
HalderADeyDSadhuAK. Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review. J Digit Imaging2020; 33: 655–677.
43.
ShankaraCHariprasadSA. Noise removal techniques for lung cancer CT images. Indian J Sci Technol2022; 15: 1577–1586.
44.
SainiHSainiDS. Enhancing classification of lung diseases by optimizing training hyperparameters of the deep learning network. Multimed Tools Appl2024; 84: 1–25.
45.
ChristoVRENehemiahHKBrightyJ, et al.Feature selection and instance selection from clinical datasets using co-operative co-evolution and classification using random forest. IETE J Res2022; 68: 2508–2521.
46.
RajagopalRKarthickRMeenalochiniP, et al.Deep convolutional spiking neural network optimized with arithmetic optimization algorithm for lung disease detection using chest X-ray images. Biomed Signal Process Control2023; 79: 104197.
47.
LuFZhangZZhaoS, et al.CMM: a CNN-MLP model for COVID-19 lesion segmentation and severity grading. IEEE/ACM Trans Comput Biol Bioinf2023; 21: 789–802
48.
ZaidiSZainabYousufMAkramU, et al.Lung segmentation-based pulmonary disease classification using deep neural networks. IEEE Access2021; 9: 125202–125214.
49.
urRehmanANaseerAKarimS, et al.Deep learning classifiers for computer-aided diagnosis of multiple lungs disease. J Xray Sci Technol2023; 31: 1125–1143.
50.
Betshrine RachelRKhanna NehemiahHSinghVK, et al.Diagnosis of COVID-19 from CT slices using whale optimization algorithm, support vector machine and multi-layer perceptron. J Xray Sci Technol2024; 32: 253–269.
51.
XiaoXYanMBasodiS, et al.Efficient hyperparameter optimization in deep learning using a variable length genetic algorithm, arXiv preprint arXiv:2006.12703, 2020.
52.
YangYZhangLDuM, et al.A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions. Comput Biol Med2021; 139: 104887.
53.
AlamMURahmaniR. Fedsepsis: a federated multi-modal deep learning-based internet of medical things application for early detection of sepsis from electronic health records using raspberry pi and jetson nano devices. Sensors2023; 23: 70.
