Deep learning-based comprehensive review on pulmonary tuberculosis

被引:0
|
作者
Twinkle Bansal
Sheifali Gupta
Neeru Jindal
机构
[1] Chitkara University,Institute of Engineering and Technology
[2] TIET,Faculty, ECED
来源
关键词
Tuberculosis (TB); Convolutional neural networks (CNN); Chest X-ray (CXR); Computer-aided diagnostics (CAD); Deep learning;
D O I
暂无
中图分类号
学科分类号
摘要
In areas with high tuberculosis (TB) prevalence, high mortality rate has significantly increased over the past few decades. Even though tuberculosis can be treated, areas with high disease burden continue to have insufficient screening tools, leading to diagnostic delays and incorrect diagnoses. As a result of these challenges, a computer-aided diagnostics (CAD) system has been developed that can automatically detect tuberculosis. There are few different methods that can be used to screen for tuberculosis; however, chest X-ray (CXR) is most commonly used and strongly suggested because it is so effective in identifying lung irregularities. Over past ten years, we have seen a meteoric rise in amount of research conducted into application of machine learning strategies to examination of chest X-ray images for screening regarding pulmonary abnormalities. Particularly, we have also noticed significant interest in testing for TB. This attentiveness has increased in tandem with phenomenal progress that has been made in deep learning (DL), which is predominately founded on convolutional neural networks (CNNs). Because of these advancements, significant research contributions have been made in field of DL techniques for TB screening by utilizing CXR images. The main focus of this paper is to emphasize favorable methods and data collection, as well as methodological contributions, identify data collections, and identify challenges.
引用
收藏
页码:6513 / 6530
页数:17
相关论文
共 50 条
  • [1] Deep learning-based comprehensive review on pulmonary tuberculosis
    Bansal, Twinkle
    Gupta, Sheifali
    Jindal, Neeru
    [J]. NEURAL COMPUTING & APPLICATIONS, 2024, 36 (12): : 6513 - 6530
  • [2] A Comprehensive Review on Deep Learning-Based Data Fusion
    Hussain, Mazhar
    O'Nils, Mattias
    Lundgren, Jan
    Mousavirad, Seyed Jalaleddin
    [J]. IEEE Access, 2024, 12 : 180093 - 180124
  • [3] Deep Learning-based Text Classification: A Comprehensive Review
    Minaee, Shervin
    Kalchbrenner, Nal
    Cambria, Erik
    Nikzad, Narjes
    Chenaghlu, Meysam
    Gao, Jianfeng
    [J]. ACM COMPUTING SURVEYS, 2022, 54 (03)
  • [4] A Comprehensive Review on Deep Learning-Based Generative Linguistic Steganography
    Badawy, Israa Lotfy
    Nagaty, Khaled
    Hamdy, Abeer
    [J]. LEARNING IN THE AGE OF DIGITAL AND GREEN TRANSITION, ICL2022, VOL 1, 2023, 633 : 651 - 660
  • [5] A Comprehensive Review of Deep Learning-Based PCB Defect Detection
    Chen, Xing
    Wu, Yonglei
    He, Xingyou
    Ming, Wuyi
    [J]. IEEE ACCESS, 2023, 11 : 139017 - 139038
  • [6] A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
    Hamishebahar, Younes
    Guan, Hong
    So, Stephen
    Jo, Jun
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (03):
  • [7] A comprehensive review of deep learning-based variant calling methods
    Ren, Junjun
    Zhang, Zhengqian
    Wu, Ying
    Wang, Jialiang
    Liu, Yongzhuang
    [J]. BRIEFINGS IN FUNCTIONAL GENOMICS, 2024, 23 (04) : 303 - 313
  • [8] Deep learning-based welding image recognition: A comprehensive review
    Liu, Tianyuan
    Zheng, Pai
    Bao, Jinsong
    [J]. JOURNAL OF MANUFACTURING SYSTEMS, 2023, 68 : 601 - 625
  • [9] A deep learning-based algorithm for pulmonary tuberculosis detection in chest radiography
    Chen, Chiu-Fan
    Hsu, Chun-Hsiang
    Jiang, You-Cheng
    Lin, Wen-Ren
    Hong, Wei-Cheng
    Chen, I. -Yuan
    Lin, Min-Hsi
    Chu, Kuo-An
    Lee, Chao-Hsien
    Lee, David Lin
    Chen, Po-Fan
    [J]. SCIENTIFIC REPORTS, 2024, 14 (01):
  • [10] A comprehensive review on deep learning-based methods for video anomaly detection
    Nayak, Rashmiranjan
    Pati, Umesh Chandra
    Das, Santos Kumar
    [J]. IMAGE AND VISION COMPUTING, 2021, 106