Implementing a Local Binary Fitting Median Filter for noise reduction in lung image datasets and subsequent classification
Abstract
Classifying medical data is one of the most challenging issues in research, largely due to its significant commercial relevance in health analytics. By labeling data through classification, we can achieve more efficient and productive analyses. Research indicates that the quality of features can adversely affect the effectiveness of this categorization. In response, this study proposes a new method called the Modified Local Binary Fitting Median Filter with Artificial Neural Network (LBFMF/ANN) to identify relevant feature subsets for detecting whether an individual has lung disease. This algorithm combines the deterministic and mathematical properties of the Local Binary Fitting Median Filter with the capabilities of Artificial Neural Networks, a deep learning technique that enhances the accuracy of lung disease predictions. The study explores the impact of feature selection on its effectiveness, emphasizing the importance of selecting relevant features from a database when analyzing samples of lung disease. The proposed classification mechanism demonstrates promising results, achieving an accuracy of 87.30%, sensitivity of 87.50%, specificity of 87.50%, and better precision than existing methods. A statistical analysis of accuracy metrics and processing times reveals that the proposed system outperforms traditional methods.
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Copyright (c) 2024 Ms. C. Keerthana, Dr. B. Azhagusundari (Author)
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