Predicting Time to Event Outcomes in Lung Cancer Detection Using Deep CNN

Lung cancer remains one of the most lethal malignancies worldwide, with early detection and accurate prognosis playing crucial roles in improving survival rates. Predicting time to event outcomes, like the progression of cancer or patient survival, is essential for personalizing treatment strategies. It is a deep learning-based method using convolutional neural network CNN for predicting time to event outcomes in lung cancer diagnosis. SVM is chosen for its robust performance in high dimensional spaces and effective handling of small sample sizes. The prediction accuracy of the SVM model is compared with that of a deep CNN to evaluate the strengths and weaknesses of each method. The dataset comprises clinical, genetic, and imaging data from lung cancer patients, preprocessed for model training and validation. Performance metrics such as accuracy, precision, recall, and the area under the ROC curve (AUC) are used to access both models.