Treffer: Role of Optimal Features Selection with Machine Learning Algorithms for Chest X-ray Image Analysis.

Introduction: The objective of the present study is to classify chest X-ray (CXR) images into COVID-positive and normal categories with the optimal number of features extracted from the images. The successful optimal feature selection algorithm that can represent images and the classification algorithm with good classification ability has been determined as the result of experiments.
Materials and Methods: This study presented a framework for the automatic detection of COVID-19 from the CXR images. To enhance small details, textures, and contrast of the images, contrast limited adaptive histogram equalization was used. Features were extracted from the first-order statistics, Gray-Level Co-occurrence Matrix, Gray-Level Run Length Matrix, local binary pattern, Law's Texture Energy Measures, Discrete Wavelet Transform, and Zernikes' Moments using an image feature extraction tool "pyFeats. For the feature selection, three nature-inspired optimization algorithms, Grey Wolf Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm, were used. For classification, Random Forest classifier, K-Nearest Neighbour classifier, support vector machine (SVM) classifier, and light gradient boosting model classifier were used.
Results and Discussion: For all the feature selection methods, the SVM classifier gives the most accurate and precise result compared to other classification models. Furthermore, in feature selection methods, PSO gives the best result as compared to other methods for feature selection. Using the combination of the SVM classifier with the PSO method, it was observed that the accuracy, precision, recall, and F1-score were 100%.
Conclusion: The result of the study indicates that with optimal features with the best choice of the classifier algorithm, the most accurate computer-aided diagnosis of CXR can be achieved. The approach presented in this study with optimal features may be utilized as a complementary tool to assist the radiologist in the early diagnosis of disease and making a more accurate decision.
(Copyright: © 2023 Journal of Medical Physics.)

There are no conflicts of interest. Supplementary Table 1Extracted features from X-raysFeature extracted fromFeatures extracted (features name given here as mentioned in zoofs python library)FOS features“FOS_Mean,” “FOS_Variance,” “FOS_Median,” “FOS_Mode,” “FOS_Skewness,” “FOS_Kurtosis,” “FOS_Energy,” “FOS_Entropy,” “FOS_MinimalGrayLevel,” “FOS_MaximalGrayLevel,” “FOS_CoefficientOfVariation,” “FOS_10Percentile,” “FOS_25Perce-ntile,” “FOS_75Percentile,” “FOS_90Percentile,” “FOS_HistogramWidth”GLCM“GLCM_ASM,” “GLCM_Contrast,” “GLCM_Correlation,” “GLCM_SumOfSquaresVariance,” “GLCM_InverseDifferenceMoment,” “GLCM_SumAverage,” “GLCM_SumVariance,” “GLCM_SumEntropy,” “GLCM_Entropy,” “GLCM_DifferenceVariance,” “GLCM_DifferenceEntropy,” “GLCM_Information1,” “GLCM_Information2,” “GLCM_MaximalCorrelationCoefficient”LTE“LTE_LL_7,” “LTE_EE_7,” “LTE_SS_7,” “LTE_LE_7,” “LTE_ES_7,” “LTE_LS_7”GLRLM“GLRLM_ShortRunEmphasis,” “GLRLM_LongRunEmphasis,” “GLRLM_GrayLevelNo-Uniformity,” “GLRLM_RunLengthNonUniformity”, “GLRLM_RunPercentage”, “GLRLM_LowGrayLevelRunEmphasis”, “GLRLM_HighGrayLevelRunEmphasis,” “GLRLM_Short owGrayLevelEmphasis,” “GLRLM_ShortRunHighGrayLevelEmphasis,” “GLRLM_LongRunLowGrayLevelEmphasis,” “GLRLM_LongRunHighGrayLevelEmphasis”LBP“LBP_R_1_P_8_energy,” “LBP_R_1_P_8_entropy,” “LBP_R_2_P_16_energy,” “LBP_R_2_P_16_entropy,” “LBP_R_3_P_24_energy,” “LBP_R_3_P_24_entropy”DWT“DWT_bior3.3_level_1_da_mean,” “DWT_bior3.3_level_1_da_std,” “DWT_bior3.3_level_1_dd_mean,” “DWT_bior3.3_level_1_dd_std,” “DWT_bior3.3_level_1_ad_mean,” “DWT_bior3.3_level_1_ad_std,” “DWT_bior3.3_level_2_da_mean,” “DWT_bior3.3_level_2_da_std,” “DWT_bior3.3_level_2_dd_mean,” “DWT_bior3.3_level_2_dd_std,” “DWT_bior3.3_level_2_ad_mean,” “DWT_bior3.3_level_2_ad_std,” “DWT_bior3.3_level_3_da_mean,” “DWT_bior3.3_level_3_da_std,” “DWT_bior3.3_level_3_dd_mean,” “DWT_bior3.3_level_3_dd_std,” “DWT_bior3.3_level_3_ad_mean,” “DWT_bior3.3_level_3_ad_std”ZM“zenikes_0,” “zenikes_1,” “zenikes_2,” “zenikes_3,” “zenikes_4,” “zenikes_5,” “zenikes_6,” “zenikes_7,” “zenikes_8,” “zenikes_9,” “zenikes_10,” “zenikes_11,” “zenikes_12,” “zenikes_13,” “zenikes_14,” “zenikes_15,” “zenikes_16,” “zenikes_17,” “zenikes_18,” “zenikes_19,” “zenikes_20,” “zenikes_21,” “zenikes_22,” “zenikes_23,” “zenikes_24”FOS: First-order statistics, GLCM: Gray-level co-occurrence matrix, GLRLM: Gray-level run length matrix, LBP: Local binary pattern, DWT: Discrete wavelet transform, ZM: Zernike’s moments, LTE: Law’s Texture Energy