Advancing Clinical Outcomes through Diverse Machine Learning Techniques In Pulmonary Nodule Detection

Abstract

Pulmonary nodules are a major clinical concern because they are hard to see and could be cancerous. They are often signs of early-stage lung cancer.  Early and accurate detection is very important for making things better for patients.  New developments in machine learning (ML) have shown that they can change the diagnostic process in a big way by making it easier and more accurate to find pulmonary nodules in medical imaging data.  This paper looks at how different machine learning techniques, from simple supervised methods to more advanced deep learning methods, can be used to find and classify pulmonary nodules.  We look at how convolutional neural networks (CNNs), support vector machines (SVMs), k-nearest neighbours (k-NN), and ensemble learning models can be used together to find nodules in chest X-rays and computed tomography (CT) scans that are different sizes and shapes.  We also talk about the pros and cons of each method, focussing on how training data, feature selection, and the model's readability affect its ability to make diagnoses.  It is also talked about how to compare mixed models that use more than one machine learning method to get the best results.  The main point of this review is to stress how important it is to use multi-modal learning and big, labelled medical datasets to make these models work better.  We also stress how important it is to validate models through thorough clinical studies to make sure that ML models can be used in real-life healthcare situations.  In the last part of the paper, future trends in finding pulmonary nodules are talked about.