Machine Learning-Based Classification of Lung Cancer Types from Radiological Images

This study develops an algorithm for classification that utilizes machine learning that uses medical images to address the urgent demand for accurate and quick lung cancer diagnosis. We use a deductive approach employing a descriptive study format while adopting an interpretivist attitude, utilizing secondary data sets for the training of models. Illustration resampling, region of interest (ROI) recognition, and radiomic extracted features are all methods used for preprocessing. The reduction of dimension as well as feature selection are aided by the principle component analysis (PCA). The model, which uses support vector machines (SVMs) as well as convolutional deep neural networks (CNNs), performs better than others in classifying different lung cancer subtypes. The stability and universality of the model are confirmed by cross-validation findings. High levels of agreement when compared to leading radiologists show the model's clinical promise. Model generalizations, interpretability, and computing efficiency are all significantly improved by feature selection including dimensionality reduction. The report advises more research into longitudinal evaluations clinical execution trials, and multiple communication data integration. The importance of addressing ethical issues and guaranteeing adaptation to changing diagnostic criteria cannot be overstated. This study provides the groundwork for revolutionary improvements in lung cancer being diagnosed, with broad repercussions for more individualized treatment plans and better patient outcomes.