DEEP LEARNING BASED MUTLI CLASS DISEASES CLASSIFICATION FROM CHEST X-RAY IMAGES
Keywords:
Multi-class classification, Disease classification, Chest X-ray images, Diagnostic accuracy, Pneumonia diagnosis, Misinterpretation rates, British Medical Journal (BMJ), Radiologist experience, Image recognitionAbstract
Chest X-rays are indispensable for diagnosing a wide range of diseases, play a pivotal role in diagnosing various diseases, but traditional methods relying on visual interpretation can introduce subjectivity and inefficiency. For instance, diagnosing pneumonia, especially among pediatric patients, poses significant challenges. but these methods can be subjective and inefficient. According to a study published in the British Medical Journal (BMJ), misinterpretation rates for chest X-rays range from 2% to 20%, depending on the condition and the experience of the radiologist. In response to these challenges, this study proposes a novel approach utilizing Convolutional Neural Networks (CNNs) to enhance diagnostic accuracy. CNNs have demonstrated remarkable capabilities in image recognition tasks, as evidenced by their widespread use in various fields, including medical imaging. By harnessing the power of CNNs, this method aims to mitigate the impact of human subjectivity and improve the efficiency of disease diagnosis. This has the potential to revolutionize pulmonary disease diagnosis, leading to faster treatment initiation and better patient outcomes. Furthermore, the scalability of CNN-based approaches allows for broader implementation across healthcare facilities, thereby benefiting a larger population. This represents a significant advancement in the field of radiology and has the potential to optimize healthcare resources and improve overall healthcare delivery.
Downloads
References
Eswara Rao, G. V., and B. Rajitha. "HQF-CC: hybrid framework for automated respiratory
disease detection based on quantum feature extractor and custom classifier model using chest
X-rays." International Journal of Information Technology (2024): 19.
Koyyada, Shiva Prasad, and Thipendra P. Singh. "A Systematic Survey of Automatic Detection
of Lung Diseases from Chest X-Ray Images: COVID-19, Pneumonia, and Tuberculosis." SN
Computer Science 5, no. 2 (2024): 229.
Hussein, Ahmad MohdAziz, Abdulrauf Garba Sharifai, Osama Moh’D. Alia, Laith Abualigah,
Khaled H. Almotairi, Sohaib KM Abujayyab, and Amir H. Gandomi. "Auto-detection of the
coronavirus disease by using deep convolutional neural networks and X-ray
photographs." Scientific reports 14, no. 1 (2024): 534.[4] Khalif, Ku Muhammad Naim Ku, Woo Chaw Seng, Alexander Gegov, Ahmad Syafadhli Abu
Bakar, and Nur Adibah Shahrul. "Integrated Generative Adversarial Networks and Deep
Convolutional Neural Networks for Image Data Classification: A Case Study for COVID-
" Information 15, no. 1 (2024): 58.
Parthasarathy, V., and S. Saravanan. "Computer aided diagnosis using Harris Hawks optimizer
with deep learning for pneumonia detection on chest X-ray images." International Journal of
Information Technology (2024): 1-7.
Mirza, Olfat M., and Ahmed H. Samak. "Neutrosophic Fuzzy Logic-Based Hybrid CNN-LSTM
for Accurate Chest X-ray Classification in COVID-19 Prediction." Sciences 18, no. 1 (2024):
Malik, Hassaan, and Tayyaba Anees. "Federated learning with deep convolutional neural
networks for the detection of multiple chest diseases using chest x-rays." Multimedia Tools and
Applications (2024): 1-29.
Adjei-Mensah, Isaac, Xiaoling Zhang, Isaac Osei Agyemang, Sophyani Banaamwini Yussif,
Adu Asare Baffour, Bernard Mawuli Cobbinah, Collins Sey, Linda Delali Fiasam, Ijeoma
Amuche Chikwendu, and Joseph Roger Arhin. "Cov-Fed: Federated learning-based framework
for COVID-19 diagnosis using chest X-ray scans." Engineering Applications of Artificial
Intelligence 128 (2024): 107448.
Islam, Md Khairul, Md Mahbubur Rahman, Md Shahin Ali, S. M. Mahim, and Md Sipon Miah.
"Enhancing lung abnormalities diagnosis using hybrid DCNN-ViT-GRU model with
explainable AI: A deep learning approach." Image and Vision Computing (2024): 104918.
Jiang, Xiaoben, Yu Zhu, Yatong Liu, Gan Cai, and Hao Fang. "TransDD: A transformer-based
dual-path decoder for improving the performance of thoracic diseases classification using chest
X-ray." Biomedical Signal Processing and Control 91 (2024): 105937.
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
