Enhancing Lung Disease Diagnosis Through A Hybrid Deep Learning Approach Pro Chest X-Ray Image Classification
Keywords:
Chest X-ray images, deep learning, Visual geometry group, Diagnosis improvement, Performance metricsAbstract
Lung disorders have a wide-reaching influence, resulting in reduced lung function and a variety of complications, such as breathing difficulty, airway blockages, and exhalation problems. Due to limited resources for lab tests and imaging procedures, early diagnosis of illnesses such as pneumonia, fibrosis, etc., remains difficult. The use of chest X-ray pictures for quick disease monitoring, crucial for ICU patients, has gained popularity due to this problem, and image processing & machine learning models have become more popular. Deep learning for lung disease detection entails three critical steps: picture pre-processing, training, and classification. Relevant features indicative of lung disorders are extracted utilizing a range of deep learning methodologies such as CNNs, RNNs, Attention Mechanisms, Transfer Learning, GANs, and VGG architectures after improving the raw quality of X-ray images by optimal filtering techniques. While typical CNNs may struggle with complicated characteristics, potentially compromising lung cancer classification accuracy, a unique method has been developed via hybrid VGG-CNN architecture. This hybrid architecture captures local and global elements; whereas CNNs excel at detail-oriented aspects, VGG networks efficiently capture wider patterns. The effectiveness of this methodology is demonstrated using open datasets that include NIH Chest X-ray data. The classification of the gathered CNN features is so, therefore, performed using Random Forest and Support Vector Machine models. A variety of systems of measurement, such as accuracy, precision, recall, and F-measure, are used to evaluate this method's effectiveness. The normal CNNVGG-SVM model's accuracy of 93.54% is significantly outperformed by the hybrid SVM-RF model's outstanding accuracy of 97.89%, a gain of 4.35%. Similarly, the hybrid RF model achieves an accuracy of 98.99%, outperforming the standard CNNVGG-RF model by 4.32%, or 94.89%. These metrics thoroughly evaluate the methodology's capacity to diagnose various lung illnesses reliably. The effectiveness of the suggested technique is demonstrated by its exceptional accuracy in improving lung disease diagnosis.
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References
Bousquet, J. Global Surveillance, “Prevention and Control of Chronic Respiratory Diseases”; World Health Organization: Geneva, Switzerland, 2007; pp. 12–36.
Smith DS, Richey EA, Brunetto WL (2020). "A symptom-based rule for diagnosis of COVID-19". SN Compr Clin Med 2:1947–1954.
Elibol E (2020),“Otolaryngological symptoms in COVID-1”9. Eur Arch Otorhinolaryngol 278:1233–1236.
Padda I, Khehra N, Jaferi U, Parmar MS (2020). "The neurological complexities and prognosis of COVID-19”. SN Compr Clin Med 2:2025–2036.
Sharma R, Agarwal M, Gupta M, Somendra S, Saxena SK (2020), “Clinical characteristics and differential clinical diagnosis of novel coronavirus disease 2019 (COVID-19). In: Saxena S. (eds) coronavirus disease 2019 (COVID-19)”. Medical virology: from pathogenesis to disease control. Springer, Singapore.
Jadhav SP, Singh H, Hussain S, Gilhotra R, Mishra A, Prasher P, Krishnan A, Gupta G (2021),"Introduction to lung diseases. In Dua K, Löbenberg R, Malheiros Luzo ÂC, Shukla S, Satija S (eds) Targeting cellular Signalling pathways in lung diseases”. Springer, Singapore.
Yahiaoui, A.; Er, O.; Yumusak, N.,”A new method of automatic recognition for tuberculosis disease diagnosis using support vector machines”. Biomed. Res. 2017, 28, 4208–4212.
Hu, Z.; Tang, J.; Wang, Z.; Zhang, K.; Zhang, L.; Sun, Q.,”Deep learning for image-based cancer detection and diagnosis-A survey”. Pattern Recognit. 2018, 83, 134–149.
American Thoracic Society. “Diagnostic Standards and Classification of Tuberculosis in Adults and Children." Am. J. Respir. Crit. Care Med. 2000, 161, 1376–1395.
Setio, A.A.A.; Traverso, A.; de Bel, T.; Berens, M.S.; van den Bogaard, C.; Cerello, P.; Chen, H.; Dou, Q.;Fantacci, M.E.; Geurts, B.; et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med. Image Anal. 2017, 42, 1–13
Razzak MI, Naz S, Zaib A. “Deep learning for medical image processing: Overview, challenges and the future”. Lect Notes Comput Vis Biomech. 2018;26:323–50.
Abiyev RH, Ma’aitah MKS (2018) “Deep convolutional neural networks for chest diseases detection”. J Healthc Eng 2018:1–11.
Varela-Santos S, Melin P (2020) “Classification of X-ray images for pneumonia detection using texture features and neural networks”. In: Castillo O, Melin P, Kacprzyk J (eds) Intuitionistic and type-2 fuzzy logic enhancements in neural and optimization algorithms: theory and applications. Studies in computational intelligence, vol 862. Springer, Cham.
Wang K, Zhang X, Huang S, Chen F (2019) "Automatic detection of pneumonia in chest X-ray images using cooperative convolutional neural networks". In: Lin Z et al. (eds) Pattern Recognition and Computer Vision. PRCV 2019. Lecture notes in computer science, vol 11858. Springer, Cham
Thakur S, Goplani Y, Arora S, Upadhyay R, Sharma G (2021) “Chest X-ray images based automated detection of pneumonia using transfer learning and CNN”. In: Bansal P, Tushir M, Balas V, Srivastava R (eds) Proceedings of international conference on artificial intelligence and applications. Advances in intelligent systems and computing, vol 1164. Springer, Singapore.
Angeline R, Mrithika M, Raman A, Warrier P (2020) “Pneumonia detection and classification using chest X-ray images with convolutional neural network. In: Smys S, Iliyasu AM, Bestak R, Shi F (eds) New trends in computational vision and bio-inspired computing. ICCVBIC 2018. Springer, Cham.
Sarkar R, Hazra A, Sadhu K, Ghosh P (2020) “A novel method for pneumonia diagnosis from chest X-ray images using deep residual learning with separable convolutional networks”. In: Gupta M, Konar D, Bhattacharyya S, Biswas S (eds) Computer vision and machine intelligence in medical image analysis. Advances in intelligent systems and computing, vol 992. Springer, Singapore.
Nath M, Choudhury C (2020) “Automatic detection of pneumonia from chest X-rays using deep learning”. In:Bhattacharjee A, Borgohain S, Soni B, Verma G, Gao XZ (eds) Machine learning, image processing, network security and data sciences. MIND 2020. Communications in computer and information science, vol1240. Springer, Singapore.
Hashmi MF, Katiyar S, Keskar AG, Bokde ND, Geem ZW (2020) "Efficient pneumonia detection in chest X-ray images using deep transfer learning ."Diagnostics 10(6):417.
Habib N, Hasan MM, Reza MM, Rahman MM (2020) “Ensemble of CheXNet and VGG-19 feature extractor with random Forest classifier for pediatric pneumonia detection”. SN Comput Sci 1:359.
Abbas A, Abdelsamea MM, Gaber MM (2020) “Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network”. Appl Intell 51:854–864.
Dansana D, Kumar R, Bhattacharjee A, Hemanth DJ, Gupta D, Khanna A, Castillo O (2020) “Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm”. Soft Comput 27:2635–2643.
Apostolopoulos ID, Mpesiana TA (2020) "Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks". Phys Eng Sci Med 43:635–640.
Turkoglu M (2020) “COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble”. Appl Intell 51:1213–1226.
Hira S, Bai A, Hira S (2020) “An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images”. Appl Intell 51:2864–2889.
Chen B, Li J, Lu G, Yu H, Zhang D (2020) “Label co-occurrence learning with graph convolutional networks for multi-label chest X-ray image classification”. IEEE J Biomed Health Inf 1–1.
Yamac M, Ahishali M, Degerli A, Kiranyaz S, Chowdhury MEH, Gabbouj M (2021) “Convolutional sparse support estimator-based COVID-19 recognition from X-ray images”. IEEE Trans Neural Netw Learn Syst 32(5):1810–1820.
Ohata EF, Bezerra GM, das Chagas JVS, Lira Neto AV, Albuquerque AB, de Albuquerque VHC, Reboucas Filho PP (2020) “Automatic detection of COVID-19 infection using chest X-ray images through transfer learning”. IEEE/CAA J Autom Sin 8:1–10.
Nishio M, Noguchi S, Matsuo H, Murakami T (2020) "Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: a combination of data augmentation methods". Sci Rep 10(1):17532.
Hemdan, E.E.D.; Shouman, M.A.; Karar, M.E. “Covidx-net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images”. arXiv 2020, arXiv:2003.11055.
Narin, A.; Kaya, C.; Pamuk, Z. “Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks”. Pattern Anal. Appl. 2021, 24, 1207–1220.
Brunese, L.; Mercaldo, F.; Reginelli, A.; Santone, “A. Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays”. Comput. Methods Programs Biomed. 2020, 196, 105608.
Ghoshal, B.; Tucker, A. “Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection”. arXiv 2020, arXiv:2003.10769.
NIH sample Chest X-rays dataset. https://www.kaggle.com/nih-chest-xrays/sample.
Padda I, Khehra N, Jaferi U, Parmar MS (2020) The neurological complexities and prognosis of COVID-19. SN Compr Clin Med 2:2025–2036.
O’Shea, K.; Nash, R. An Introduction to Convolutional Neural Networks. arXiv 2015, arXiv:1511.08458v2.
Ker, J.; Wang, L. Deep Learning Applications in Medical Image Analysis. IEEE Access 2018, 6, 9375–9389.
Al-Ajlan, A.; Allali, A.E. CNN—MGP: Convolutional Neural Networks for Metagenomics Gene Prediction. Interdiscip. Sci. Comput. Life Sci. 2019, 11, 628–635.
Pan, S.J.; Yang, Q. A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 2010, 22, 1345–1359.
Lopes, U.K.; Valiati, J.F. Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Comput. Biol. Med. 2017, 89, 135–143
Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556.
A. M. Alqudah, and M. A. Alqudah, “Sliding Window Based Support Vector Machine System for Classification of Breast Cancer Using Histopathological Microscopic Images,” IETE Journal of Research, vol. 7, pp. 1-9, 2019, doi: 10.1080/03772063.2019.1583610.
A. M. Alqudah, “Ovarian Cancer Classification Using Serum Proteomic Profiling and Wavelet Features A Comparison of Machine Learning and Features Selection Algorithms,” Journal of Clinical Engineering, vol. 44, no. 4, pp. 165-173, 2019, doi: 10.1097/JCE.0000000000000359.
M. A. Alqudah, A. Albadarneh, I. Abu-Qasmieh, and H. Alquran, “Developing of robust and high accurate ECG beat classification by combining Gaussian mixtures and wavelets features,” Australasian physical & engineering sciences in medicine, vol. 42, no. 1, pp. 149-157, 2019, doi: 10.1007/s13246-019-00722-z
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