Predicting Lung Diseases Using Machine Learning on X-ray Images

Diseases of the lung including lung cancer, pneumonia, and even tuberculosis are considered among the leading diseases that contribute to global mortality and morbidity.1-3 The existing diagnostic methodologies primarily entail a lot of interpretation labour that is not only tedious but also quite subjective and prone to fatigue of the interpreters. In this undertaking, we examined the possibility of employing machine learning (ML) methods for the correct classification of lung diseases, particularly focusing on chest X-ray images as an important medical image feature set. The aim of this study then is the development of a machine-learning model capable of auto-classifying various types of lung diseases within X-ray images. A Convolutional Neural Network (CNN) was employed to extract features and for classification, as it can learn complex patterns in medical images. The dataset was pre-processed by resizing, normalizing, and augmenting the X-ray images to make the model more robust. The developed model was trained with a large public dataset and the performance of the model was evaluated using accuracy, precision, recall and F1 score [1]. Our research’s experiments uniquely allow us to conclude that the CNN model is rather efficient when it comes to classifying different types of lung conditions which would assist the radiologists in their diagnostic activities. The results emphasize the role of machine learning in imaging as it improves the accuracy of the diagnosis and helps in faster and more efficient diagnosis of lung diseases. This is a by far pioneering study that would be particularly useful for patients residing in regions where the availability of professional radiologists is low. With the implementation of some ML-based interventions, medical facilities may be able to improve their capacity for timely lung disease detection which would ultimately help with patient management.