TY - JOUR
T1 - Intelligent Control Techniques for the Detection of Biomedical Ear Infections
AU - Abdulaal, Mohammed J.
AU - Mehedi, Ibrahim M.
AU - Aljohani, Abdulah Jeza
AU - Milyani, Ahmad H.
AU - Mahmoud, Mohamed
AU - Sahu, Manish Kumar
AU - Abusorrah, Abdullah M.
AU - Meem, Rahtul Jannat
N1 - Publisher Copyright:
© 2022 Mohammed J. Abdulaal et al.
PY - 2022
Y1 - 2022
N2 - The capacity to carry out one's regular tasks is affected to varying degrees by hearing difficulties. Poorer understanding, slower learning, and an overall reduction in efficiency in academic endeavours are just a few of the negative impacts of hearing impairments on children's performance, which may range from mild to severe. A significant factor in determining whether or not there will be a decrease in performance is the kind and source of impairment. Research has shown that the Artificial Neural Network technique is capable of modelling both linear and nonlinear solution surfaces in a trustworthy way, as demonstrated in previous studies. To improve the precision with which hearing impairment challenges are diagnosed, a neural network backpropagation approach has been developed with the purpose of fine-tuning the diagnostic process. In particular, it highlights the vital role performed by medical informatics in supporting doctors in the identification of diseases as well as the formulation of suitable choices via the use of data management and knowledge discovery. As part of the intelligent control method, it is proposed in this research to construct a Histogram Equalization (HE)-based Adaptive Center-Weighted Median (ACWM) filter, which is then used to segment/detect the OM in tympanic membrane images using different segmentation methods in order to minimise noise and improve the image quality. A tympanic membrane dataset, which is freely accessible, was used in all experiments.
AB - The capacity to carry out one's regular tasks is affected to varying degrees by hearing difficulties. Poorer understanding, slower learning, and an overall reduction in efficiency in academic endeavours are just a few of the negative impacts of hearing impairments on children's performance, which may range from mild to severe. A significant factor in determining whether or not there will be a decrease in performance is the kind and source of impairment. Research has shown that the Artificial Neural Network technique is capable of modelling both linear and nonlinear solution surfaces in a trustworthy way, as demonstrated in previous studies. To improve the precision with which hearing impairment challenges are diagnosed, a neural network backpropagation approach has been developed with the purpose of fine-tuning the diagnostic process. In particular, it highlights the vital role performed by medical informatics in supporting doctors in the identification of diseases as well as the formulation of suitable choices via the use of data management and knowledge discovery. As part of the intelligent control method, it is proposed in this research to construct a Histogram Equalization (HE)-based Adaptive Center-Weighted Median (ACWM) filter, which is then used to segment/detect the OM in tympanic membrane images using different segmentation methods in order to minimise noise and improve the image quality. A tympanic membrane dataset, which is freely accessible, was used in all experiments.
UR - http://www.scopus.com/inward/record.url?scp=85137895469&partnerID=8YFLogxK
U2 - 10.1155/2022/9653513
DO - 10.1155/2022/9653513
M3 - Article
C2 - 36105634
AN - SCOPUS:85137895469
SN - 1687-5265
VL - 2022
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
M1 - 9653513
ER -