Lung Disease Identification Based on Chest X-ray and Lung Sounds Using Machine Learning and Deep Learning Techniques

Sanjeevkumar Hatture, Madhu Koravanavar, Rashmi Saini


In recent years there is heavy demand in healthcare systems due to COVID-19 pandemic. The COVID-19 will mainly cause the Lung infections, which affected the whole world very badly during last two years and still continues to affecting the world, this causes problem to the life of common man.  To overcome such problems and also in order to identify the type of the lung disease, and diagnosing abnormalities in the lung area the Chest X-Rays (CXRs) and Lung Sounds are most commonly used medical testings. Accurate identification of diseases helps in saving the life of a human from diseases like covid-19, pneumonia, TB, lung cancer etc. The commonly used medical testings are cost effective and which are very helpful in early diagnosis of pulmonary diseases. The most difficult task for radiologists and pulmonologists is to classify the pulmonary diseases using images of X-rays and Lung sounds. To identify the lung diseases, Computer Aided Diagnosis (CAD) systems assist doctors in identifying underlying diseases. Due to less availability of skilled radiologists and lung sound recording devices will make the situation of the patients more worse. The goal is to resolve the problem using non clinical methods such as Machine and Deep Learning Techniques and these techniques may be very helpful in proper detection of severe respiratory diseases using lung sounds and lung X-ray images. Lung sounds provides better accuracy and also the proposed work provides the precautionary measures to prevent the Lung infections. Hence using usual medical testings and efficient techniques are capable to overcome the severity of lung diseases. So the work aims in identify the type of the Lung disease by employing the machine learning techniques viz. fuzzy logic and Convolutional neural network (CNN) in deep learning for improvement of the performance/accuracy.

Full Text:



Naman Gupta , Deepak Gupta , Ashish Khanna , Pedro P. Rebouças Filho , Victor Hugo C. de Albuquerque , “Evolutionary algorithms for automatic lung disease detection” Measurement, Sensor Systems and Applications Conference. 17 February 2019.

D. Chamberlain, R. Kodgule, D. Ganelin, V. Miglani and R. R. Fletcher, "Application of semi-supervised deep learning to lung sound analysis," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, pp. 804-807.

Souid, A.; Sakli, N.; Sakli, H. Classification and Predictions of Lung Diseases from Chest X-rays Using MobileNet V2. Appl. Sci. 2021.

F. Demir, A. M. Ismael and A. Sengur, "Classification of Lung Sounds With CNN Model Using Parallel Pooling Structure," IEEE Conference, vol. 8, pp. 105376-105383, 2020.

Rahib H. Abiyev, Abdullahi Ismail, "COVID-19 and Pneumonia Diagnosis in X-ray Images Using Convolutional Neural Networks", Mathematical Problems in Engineering, vol. 2021, Article ID 3281135, 14 pages, 2021.

Luca Brunesea , Francesco Mercaldoa , Alfonso Reginelli , Antonella Santone "Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays." Computer Methods and Programs in Biomedicine 196, 2020.

Kieu, S.T.H.; Bade, A.; Hijazi, M.H.A.; Kolivand, H. A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions. J. Imaging 2020.

Gurung A, Scrafford CG, Tielsch JM, Levine OS, Checkley W. “Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis.” Respir Med. 2011 Sep;105(9):1396-403. doi: 10.1016/j.rmed.2011.05.007. Epub 2011 Jun 14.

Jung SY, Liao CH, Wu YS, Yuan SM, Sun CT. “Efficiently Classifying Lung Sounds through Depthwise Separable CNN Models with Fused STFT and MFCC Features.” Diagnostics (Basel). 2021 Apr 20.

Stefanus Kieu Tao Hwa , Mohd Hanafi Ahmad Hijazi, Abdullah Bade, Razali Yaakob, Mohammad Saffree Jeffree. "Ensemble deep learning for tuberculosis detection using chest X-ray and canny edge detected images." IAES International Journal of Artificial Intelligence 8.4 .2019.

M. Fraiwan1, L. Fraiwan2 , M. Alkhodari3 ,O. Hassanin3. “Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory.”J Ambient Intell Human Comput 2021.

Bharati, Subrato, Prajoy Podder, and M. Rubaiyat Hossain Mondal. "Hybrid deep learning for detecting lung diseases from X-ray images." Informatics in Medicine Unlocked 20 .2020.

Geraldo Luis Bezerra Ramalho, Pedro Pedrosa Rebouças Filho, Fátima Nelsizeuma Sombra de Medeiros, Paulo César Cortez."Lung disease detection using feature extraction and extreme learning machine." Revista Brasileira de Engenharia Biomédica 30.3 .2014.

Zak, Matthew, and Adam Krzyżak. "Classification of lung diseases using deep learning models." International Conference on Computational Science. Springer, Cham, 2020.

Arpan Srivastava1 , Sonakshi Jain1 , Ryan Miranda1 , Shruti Patil2 , Sharnil Pandya2 and Ketan Kotecha2."Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease." PeerJ Computer Science 7 .2021.

Rachna Jain, Preeti Nagrath , Gaurav Kataria , V. Sirish Kaushik , D. Jude Hemanth.“Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning” sciencedirect ,may 2020.

Goyal S, Singh R. “ Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques.” J Ambient Intell Humaniz Comput. 2021.

Yoonjoo Kim1, YunKyong Hyon, Sung Soo Jung, Sunju Lee, GeonYoo, Chaeuk Chung & Taeyoung Ha2.“Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning.”Sci Rep 11, 17186. 2021.

Naik, Rasika, Mr.Tejas Wani, Mr. Shiva Ahir, Mr. Atharva Joshi. "Detection of Lung Diseases using Deep Learning." Proceedings of the 3rd International Conference on Advances in Science & Technology (ICAST). 2020.

Murat Aykanat,Özkan Kılıç a, Bahar Kurt,b , Sevgi Saryal “Lung disease classification using machine learning algorithms” International Journal of Applied Mathematics Electronics and Computers. December 2020.

Xiaosong Wang , Yifan Peng, Le Lu , Zhiyong Lu ,Ronald M. Summers "Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays." Proceedings of the IEEE conference on computer vision and pattern recognition.2018.

Georgios Petmezas , Grigorios-Aris Cheimariotis , Leandros Stefanopoulos , Bruno Rocha , Rui Pedro Paiva , Aggelos K. Katsaggelos and Nicos Maglaveras , "Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function." Sensors 22.3. 2022.

Magrelli S, Valentini P, De Rose C, Morello R and Buonsenso D (2021) Classification of Lung Disease in Children by Using Lung Ultrasound Images and Deep Convolutional Neural Network. Front. Physiol. 12:693448. doi: 10.3389/fphys.2021.693448.

Meet Diwan1, Bhargav Patel2, Jaykumar Shah “Classification of Lungs Diseases Using Machine Learning Technique” International Research Journal of Engineering and Technology(IRJET) 09 Sep 2021.

Rizwana Zulfiqar , Fiaz Majeed, Rizwana Irfan, Hafiz Tayyab Rauf , Elhadj Benkhelifa and Abdelkader Nasreddine Belkacem"Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition." Frontiers in Medicine 8, 2021.

Sanjeevakumar M. Hatture, Nagaveni Kadakol, “Clinical diagnostic systems based on machine learning and deep learning”, Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics, Academic Press, pp. 159-183, 2021.

Sanjeevakumar M. Hatture, Nagaveni Kadakol, “Identification of Intra-abdominal Organs Using Deep Learning Techniques”, In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. Lecture Notes in Networks and Systems, vol 154. Springer, Singapore, pp. 547-554, 2021.

Shanmukhappa A. Angadi, Sanjeevakumar M. Hatture, Text-Dependent Speaker Recognition System Using Symbolic Modelling of Voiceprint. In: Nagabhushan, T., Aradhya, V.N.M., Jagadeesh, P., Shukla, S., M.L., C. (eds) Cognitive Computing and Information Processing. CCIP 2017. Communications in Computer and Information Science, vol 801. Springer, Singapore, pp. 358-372, 2018.


  • There are currently no refbacks.

Abava  Кибербезопасность MoNeTec 2024

ISSN: 2307-8162