Multi-disease detection system with X-ray images using deep learning techniques
*M. Suleman BashaCorresponding authorsuleman.ndl@gmail.comDepartment of Computer Science and Engineering (DS)Rajeev Gandhi Memorial College of Engineering & TechnologyNandyal, Andhra Pradesh, 518501, IndiaView full profile → , K. Rajendra Prasadkrprgm@gmail.comDepartment of Computer Science and EngineeringInstitute of Aeronautical EngineeringHyderabad, Telangana, 500043, IndiaView full profile → , S. K. Mouleeswaranmeetmoulee@gmail.comDepartment of Computer Science and EngineeringDayananda Sagar UniversityBangalore, Karnataka, 560068, IndiaView full profile → , Ramesh Chandra Pooniarameshcpoonia@gmail.comDepartment of Computer ScienceCHRIST (Deemed to be University), Delhi NCRGhaziabad, Uttar Pradesh, 201003, IndiaView full profile → , Shiju Sebastianshijusebastian@christuniversity.inSchool of Business and ManagementCHRIST (Deemed to be University), Delhi NCRGhaziabad, Uttar Pradesh, 201003, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 09 Apr 2024
- Published Online:
- 12 Aug 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1710
- Pages:
- 1379–1388
Abstract
Keywords
Subject Classifications
References
[1] Wang, Shuo, et al. “Deep learning-based multi-disease detection system for X-ray images.” Computers in Biology and Medicine 137 (2021).
[2] Islam, Muhammad M., et al. “An automated multi-disease detection system using deep learning with X-ray images.” Expert Systems with Applications 184 (2021).
[3] Suleman Basha, M., et.al., K. Hybrid visual computing models to discover the clusters assessment of high dimensional big data. Soft Comput 27, 4249–4262 (2023).
[4] Liu, Lingxiao, et al. “A novel multi-disease detection system with deep learning from chest X-ray images.” IJ of Computer Assisted Radiology and Surgery : 1-10 (2021).
[5] Yoo, Yoon-Sik, et al. “Multi-disease detection system using transfer learning with X-ray images.” Journal of Ambient Intelligence and Humanized Computing : 1-12 (2021).
[6] Kassani, Sahar Hosseini, et al. “A hybrid deep learning approach for multi-disease detection using X-ray images.” Biocybernetics and Biomedical Engineering (2021).
[7] Basha, M.S., et.al., Sampling-based visual assessment computing techniques for an efficient social data clustering. J Supercomput 77, 8013–8037 (2021).
[8] Naz, Sana, et al. “A novel deep learning approach for multi-disease detection from chest X-ray images.” Computerized Medical Imaging and Graphics 93 (2021).
[9] Asif, Ali, et al. “Multi-disease detection system using deep learning with X-ray images: A comparative study.” Computers in Biology and Medicine 128 (2021).
[10] Basha, M.S., et.al., Detection of pre-cluster nano-tendency through multiviewpoints cosine-based similarity approach. Nanotechnol. Environ. Eng. 7, 259–268 (2022).
[11] Ozturk, Tulin, et al. “Automated detection of COVID-19 cases using deep neural networks with X-ray images.” Computers in Biology and Medicine 121 (2020).
[12] Apostolopoulos, et.al., “Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks.” Physical and Engineering Sciences in Medicine 43.2 : 635-640 (2020).
[13] Narin, Ali, et al. “Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks.” Pattern Analysis and Applications 24.3 (2021).
[14] Singh, Vijander, et al. “Prediction of COVID-19 corona virus pandemic based on time series data using Support Vector Machine.” Journal of Discrete Mathematical Sciences and Cryptography 23.8 : 1583-1597 (2020).
[15] Bhatnagar, Vaibhav, et al. “Descriptive analysis of COVID-19 patients in the context of India.” Journal of Interdisciplinary Mathematics 24.3 : 489-504 (2021).
