A Novel Image Feature Extraction Based Machine Learning approach for Disease Detection from Chest X-Ray Images
Abstract
The limitation of feature selection is the biggest challenge for machine learning classifiers in disease classification. This research proposes a novel feature extraction method to extract representative features from medical images, combining extracted features with original image pixel features. Additionally, we propose a new method that uses data values from Andrews's curve function to transform chest x-ray images into spectrograms. The spectrogram images are believed to aid in distinguishing near-similar medical images, such as COVID and pneumonia. The study aims to build an efficient machine learning system that applies the proposed feature extraction method and utilizes spectrogram images for distinguishing near-similar medical images. For experimental analysis, we have used the award winning Kaggle Chest Radiography image dataset. The test results show that among all machine learning classifiers, the logistic regression classifier could correctly distinguish COVID and pneumonia images with a 97.18% test accuracy, a 98.34% detection rate, a 97.8% precision rate, and an AUC value of 0.99 on the test dataset. The machine learning model has learned to distinguish between medical images that appear similar using features found through the proposed feature extraction and spectrogram images. The results also proved that the proposed approach using XGBoost has outperformed state-of-the-art models in recent research studies when (i) binary classification is performed using COVID-19 and Normal Chest x-ray images and (ii) multiclass classification is performed using Normal, COVID and Pneumonia Chest x-ray images.
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