Dr H Shaheen

Early diagnosis of tuberculosis using deep learning approach for iot based healthcare applications

• Authors Details :  
• G. Simi Margarat,  
• G. Hemalatha,  
• Annapurna Mishra,  
• H. Shaheen,  
• K. Maheswari,  
• S. Tamijeselvan,  
• U. Pavan Kumar,  
• V. Banupriya,  
• Alachew Wubie Ferede

Journal title : Computational Intelligence and Neuroscience

Publisher : Hindawi Limited

Print ISSN : 1687-5265

Page Number : 1-9

Journal volume : 2022

550 Views Original Article

In the modern world, Tuberculosis (TB) is regarded as a serious health issue with a high rate of mortality. TB can be cured completely by early diagnosis. For achieving this, one tool utilized is CXR (Chest X-rays) which is used to screen active TB. An enhanced deep learning (DL) model is implemented for automatic Tuberculosis detection. This work undergoes the phases like preprocessing, segmentation, feature extraction, and optimized classification. Initially, the CXR image is preprocessed and segmented using AFCM (Adaptive Fuzzy C means) clustering. Then, feature extraction and several features are extracted. Finally, these features are given to the DL classifier Deep Belief Network (DBN). To improve the classification accuracy and to optimize the DBN, a metaheuristic optimization Adaptive Monarch butterfly optimization (AMBO) algorithm is used. Here, the Deep Belief Network with Adaptive Monarch butterfly optimization (DBN-AMBO) is used for enhancing the accuracy, reducing the error function, and optimizing weighting parameters. The overall implementation is carried out on the Python platform. The overall performance evaluations of the DBN-AMBO were carried out on MC and SC datasets and compared over the other approaches on the basis of certain metrics.

Article DOI & Crossmark Data

DOI : https://doi.org/10.1155/2022/3357508

Article Subject Details

• Biomedical health research ethics
• Computer Science
• Computer Science Applications
• Decision Sciences
• Interdisciplinary science

Article Keywords Details

• Adaptive Monarch butterfly optimization
• Chest X-rays
• DL classifier Deep Belief Network (DBN)
• Metaheuristic optimization Adaptive Monarch butterfly optimization (AMBO) algorithm
• Python
• Tuberculosis (TB)

Article File

Full Text PDF

---

---

Article References

• (1). 10.1038/s41598-019-42557-4
• (2). 10.1016/j.patrec.2019.11.014
• (3). 10.1109/tmi.2017.2775636
• (4). 10.1038/srep25265
• (5). 10.1109/tmi.2013.2284099
• (6). 10.1016/j.compbiomed.2017.08.001
• (7). 10.1007/s10916-018-0991-9
• (8). 10.1109/access.2020.3031384
• (9). 10.3390/app10175749
• (10). 10.3390/sym12071146
• (11). 10.1002/ima.22556
• (12). 10.1016/j.compeleceng.2021.107252
• (13). 10.1007/s13246-020-00966-0
• (14). 10.1080/21681163.2020.1808532
• (15). 10.1155/2022/6489185
• (16). 10.1155/2022/7799812
• (17). 10.1155/2022/1332373
• (18). 10.1155/2022/1570604
• (19). 10.1155/2021/3611182
• (20). 10.1155/2021/4927607
• (21). 10.1007/s00521-015-1923-y
• (22). 10.1016/j.cell.2018.02.010
• (23). 10.1016/j.cell.2018.02.010
• (24). 10.1109/tmi.2017.2775636

---

More Article by Dr H Shaheen