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Chest Disease Detection Using Deep Learning Models
Subject area: Science,Engineering and Technology · Area of research: Deep Learning
Abstract
DL models automate the analysis of medical images through techniques like prediction, segmentation, and classification, often achieving accuracy levels that surpass human capabilities. This is particularly valuable in medical fields where early and accurate diagnosis can significantly affect treatment success rates. For instance, in breast cancer screening, DL models can identify subtle signs of cancer in mammograms more accurately and earlier than traditional methods. Similarly, in lung cancer and brain tumors, DL models facilitate the detection of minute lesions and abnormalities that might be overlooked in manual examinations. This research work focuses on the detection of several chest diseases including lymphoma disease. We have employed DTL and FL models to make the prediction procedure more accessible and faster such as VGG-19. The research's proposed system was developed to predict 14 different kinds of chest conditions, and a comparison between federated learning and deep learning models was made. The proposed study describes a system that can predict lymphoma cancer with reasonable accuracy using sample images taken by various pathologists at different locations. A dataset titled malignant lymphoma classification dataset, which contains more than five thousand images, was used to analyze this study. When the models were evaluated, it was discovered that the VGG-16 model that was suggested had the best accuracy. Consequently, the classification report states that the VGG-16 model is the most effective deep and federated transfer-learning model for lymphoma cancer classification.
Keywords
Chest Diseases, Deep Learning, X-ray, lymphoma, VGG-19, DL models
References
[1] Liu, Xiaoqing, et al. "Advances in deep learning- based medical image analysis." Health Data Science 2021 (2021).
[2] Naveen Sai Bommina, Nandipati Sai Akash, Uppu Lokesh, Dr. Hussain Syed, Dr. Syed Umar, "A Hybrid Optimization Framework for Enhancing IoT Security via AI-based Anomaly Detection", International Journal on Recent and Innovation Trends in Computing and Communication, (2023) ISSN: 2321-8169 Volume: 11 Issue: 3.
[3] K Sankar, Divya Rohatgi, S Balakrishna Reddy, "COX Regressive Winsorized Correlated Convolutional Deep Belief Boltzmann Network for Covid-19 Prediction with Big Data", Grenze International Journal of Engineering & Technology (GIJET), Grenze ID: 01.GIJET.9.1.547, 2023.
[4] Lundervold, Alexander Selvikvåg, and Arvid Lundervold. "An overview of deep learning in medical imaging focusing on MRI." Zeitschrift für MedizinischePhysik 29.2 (2019): 102-127.
[5] Uppu Lokesh , Naveen Sai Bommina , Nandipati Sai Akash , Dr. Hussain Syed , Dr. Syed Umar. (2021). Deep Reinforcement Learning with Genetic Algorithm Tuning for Intrusion Detection in IoT Systems. International Journal of Communication Networks and Information Security (IJCNIS), 13(3), 582–595.
[6] K. Kartheeban, K. Kalyani, S. K. Bommavaram, D. Rohatgi, M. N. Kathiravan, and S. Saravanan, “Intelligent Deep Residual Network based Brain Tumor Detection and Classification,” in 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS), Dec. 2022, pp. 785 –790.
[7] Lee, J. G., Jun, S., Cho, Y. W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017). Deep learning in medical imaging: general overview. Korean journal of radiology, 18(4), 570-584.
[8] Usman, M., Zubair, M., Hussein, H. S., Wajid, M., Farrag, M., Ali, S. J., ... & Habeeb, M. S. (2021). Empirical mode decomposition for analysis and filtering of speech signals. IEEE Canadian Journal of Electrical and Computer Engineering, 44(3), 343-349.
[9] Uppu Lokesh, Naveen Sai Bommina, Nandipati Sai Akash, Dr. Hussain Syed, Dr. Syed Umar, "Designing Energy-Efficient and Secure IoT Architectures Using Evolutionary Optimization Algorithms", International Journal of Applied Engineering & Technology, Vol. 4 No.2, September, 2022.
[10] Divya Rohatgi, Dr. Tulika Pandey, "Regression Test Selection Framework for Web Services", INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 03, MARCH 2020.
[11] Nasser, A. A., &Akhloufi, M. A. (2023). Deep Learning Methods for Chest Disease Detection Using Radiography Images. SN Computer Science, 4(4), 388. https://
[12] Naveen Sai Bommina , Nandipati Sai Akash, Uppu Lokesh , Dr. Hussain Syed , Dr. Syed Umar, "Multi-Objective Genetic Algorithms for Secure Routing and Data Privacy in IoT Networks", International Journal of Communication Networks and Information Security (IJCNIS), (2020), 12(3), 632–643.
[13] Glocker, B., Jones, C., Bernhardt, M., &Winzeck, S. (2023). Algorithmic encoding of protected characteristics in chest X-ray disease detection models. Ebiomedicine, 89. https://
[14] Nandipati Sai Akash, Naveen Sai Bommina, Uppu Lokesh, Hussain Syed, Syed Umar, "Optimized Block Chain-Enabled Security Mechanism for IoT Using Ant Colony Optimization", International Journal on Recent and Innovation Trends in Computing and Communication, (2023), 11(10), 1226–1233.
[15] Mr DD Sarpate, "Design of Dual Band Microstrip for satellite Applications", 2nd International Conference on Recent Innovations in Engineering & Technology 2020
[16] Bharati, S., Podder, P., & Mondal, M. R. H. (2020). "Hybrid deep learning for detecting lung diseases from X-ray images," Informatics in Medicine Unlocked, vol. 20, 100391.
[17] Singh, A., Gupta, M., Raj, A., Gupta, S. K., & Habeeb, M. S. (2020, December). TWDM-PON: The Enhanced PON for Triple Play Services. In 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (pp. 1-5). IEEE.
[18] Naveen Sai Bommina , Nandipati Sai Akash, Uppu Lokesh , Dr. Hussain Syed , Dr. Syed Umar, "Privacy-Preserving Federated Learning for IoT Devices with Secure Model Optimization", International Journal of Communication Networks and Information Security (IJCNIS), (2021), 13(2), 396–405.
[19] Guo, Y., Song, Q., Jiang, M., Guo, Y., Xu, P., Zhang, Y., ... & Yao, X. (2021). Histological subtypes classification of lung cancers on CT images using 3D deep learning and radiomics. Academic radiology, 28(9), e258-e266. https://
[20] Aftab, M. O., Awan, M. J., Khalid, S., Javed, R., & Shabir, H. (2021, April). Executing Spark BigDL for Leukemia Detection from Microscopic Images using Transfer Learning. In 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA) (pp. 216-220). IEEE
[21] Gilanie, G., Bajwa, U. I., Waraich, M. M., Asghar, M., Kousar, R., Kashif, A., ... & Rafique, H. (2021). Coronavirus (COVID-19) detection from chest radiology images using convolutional neural networks. Biomedical Signal Processing and Control, 66, 102490.
[22] Bouloumié, A., Curat, C. A., Sengenès, C., Lolmède, K., Miranville, A., & Busse, R. (2005). Role of macrophage tissue infiltration in metabolic diseases. Current Opinion in Clinical Nutrition & Metabolic Care, 8(4), 347-354.
[23] Naveen Sai Bommina, Uppu Lokesh, Nandipati Sai Akash, Dr. Hussain Syed, Dr. Syed Umar, "Optimized AI Models for Real -Time Cyberattack Detection in Smart Homes and Cities", International Journal of Applied Engineering & Technology, Vol. 4 No.1, June, 2022.
[24] Mr. Dikshendra Daulat Sarpate, and Dr. B.G Nagaraja, "CONVOLUTION NEURAL NETWORK-BASED SPEECH EMOTION RECOGNITION USING MFCCS", International Journal of Communication Networks and Information Security, 2023/12/10
[25] Habeeb, M. S., & Babu, T. R. (2022). Network intrusion detection system: a survey on artificial intelligence‐based techniques. Expert Systems, 39(9), e13066
[26] Sundaralingam, A., Bedawi, E. O., & Rahman, N. M. (2020). Diagnostics in pleural disease. Diagnostics, 10(12), 1046. https://
[27] Umar, Syed, Bommina Naveen Sai, Nagineni Sai Lasya,Doppalapudi Asutosh, and LohithaRani. "Machine Learning based Sentiment Analysis of Product Reviews Using DeepEmbedding." Journal of Optoelectronics Laser 41, no. 6(2022): 108-113.
[28] Cui, X., Chen, W., Zhou, H., Gong, Y., Zhu, B., Lv, X., ... & Ma, H. (2021). Pulmonary edema in COVID-19 patients: mechanisms and treatment potential. Frontiers in pharmacology, 12, 664349.
How to cite this paper
@article{1712444,
author = {R. Prasanth Reddy, Nagavelli Yogender Nath, Gattu Ramya, Syed Abdul Haq},
title = {Chest Disease Detection Using Deep Learning Models},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {7},
pages = {728-736},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712444.pdf},
abstract = {DL models automate the analysis of medical images through techniques like prediction, segmentation, and classification, often achieving accuracy levels that surpass human capabilities. This is particularly valuable in medical fields where early and accurate diagnosis can significantly affect treatment success rates. For instance, in breast cancer screening, DL models can identify subtle signs of cancer in mammograms more accurately and earlier than traditional methods. Similarly, in lung cancer and brain tumors, DL models facilitate the detection of minute lesions and abnormalities that might be overlooked in manual examinations. This research work focuses on the detection of several chest diseases including lymphoma disease. We have employed DTL and FL models to make the prediction procedure more accessible and faster such as VGG-19. The research's proposed system was developed to predict 14 different kinds of chest conditions, and a comparison between federated learning and deep learning models was made. The proposed study describes a system that can predict lymphoma cancer with reasonable accuracy using sample images taken by various pathologists at different locations. A dataset titled malignant lymphoma classification dataset, which contains more than five thousand images, was used to analyze this study. When the models were evaluated, it was discovered that the VGG-16 model that was suggested had the best accuracy. Consequently, the classification report states that the VGG-16 model is the most effective deep and federated transfer-learning model for lymphoma cancer classification.},
keywords = {Chest Diseases, Deep Learning, X-ray, lymphoma, VGG-19, DL models},
month = {January},
doi = {https://doi.org/10.64388/IREV7I7-1712444}
}