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- 저자 : Chandrasekaran Anitha (검색결과 2 건)
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Ramkumar Vidya,Chandrasekaran Anitha 대한청각학회 2023 Journal of Audiology & Otology Vol.27 No.2
Background and Objectives: This study describes the development of an International Classification for Functioning, Disability and Health (ICF)-based inventory for tinnitus (ICF-TINI) that measures the impact of tinnitus on the function, activities, and participation of an individual.Subjects and Methods: This cross-sectional study utilized the ICF-TINI, which included 15 items from the two ICF components of body function and activities. We included 137 respondents with chronic tinnitus. Confirmatory factor analysis validated the two-structure framework (body function, activities and participation). The model fit was assessed by comparing fit values of chi-square (df), root mean square error of approximation, comparative fit index, incremental fit index, and Tucker-Lewis index, with the suggested fit criteria values. Cronbach’s alpha was used to assess internal consistency reliability.Results: The fit indices confirmed the presence of two structures in ICF-TINI, while the factor loading values suggested each item’s goodness of fit. The ICF-internal TINI exhibited high consistency reliability (0.93).Conclusions: The ICFTINI is a reliable and valid tool for assessing the impact of tinnitus on an individual’s body function, activities, and participation.
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Brain Tumor Detection Based on Amended Convolution Neural Network Using MRI Images
Mohanasundari M,Dr. Chandrasekaran V,Dr. Anitha S 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.10
Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue pre-processing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.
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