Advanced Machine Learning Techniques for Precise Lung Cancer Detection from CT Scans

Using a hybrid machine learning model that takes into account Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Gradient Boosting methods, this research proposes a complete strategy for the early diagnosis of lung cancer. Due to the similarity in structure between healthy and sick lung tissues, diagnosing lung cancer is a difficult process, making automated technologies necessary to aid radiologists. Several datasets are used, with CT scans being the primary emphasis, and preprocessing methods such noise reduction, normalization, and lung segmentation are examined to improve picture quality for the purpose of detecting lung cancer. To precisely pinpoint lung nodules within CT scan pictures, CNNs are used in the segmentation step. Using visual feature extraction as input, SVM acts as a classification tool to distinguish between malignant and benign situations. Predicting lung cancer risk using patient data, such as medical records and genetic characteristics, is done with the use of gradient boosting algorithms like XGBoost and LightGBM. Nodule form, mean intensity, standard deviation intensity, and skewness are all part of the data that must be extracted in order to complete this stage. Principal Component Analysis (PCA) may help in feature selection, which in turn improves model interpretability and decreases over fitting. For all-inclusive lung cancer prediction, the suggested approach merges findings from Convolutional Neural Networks, Support Vector Machines, and Gradient Boosting models. The experimental findings support the efficiency of this strategy, which makes use of ensemble models, transfer learning, and feature selection to boost accuracy. The article highlights the importance of image processing and machine learning in detecting lung cancer early, which has the potential to greatly reduce deaths caused by the disease.