Optimizing CNN Architectures for Early Detection of Lung Cancer Using CT Images

Lung cancer remains a primary cause of cancer-related deaths globally, and early diagnosis is crucial to improve patient outcomes. Whole-body computed tomography (CT) imaging holds promise as a diagnostic tool for the early detection of lung cancer. Still, malignancies' highly variable and spatially complex properties require refined image analysis techniques. Although convolutional neural networks (CNNs) have become great tools for image analysis, performance relies highly on network architecture design. This project focuses on CNN architecture optimization for lung cancer detection from CT scan images at a very early stage. We use this method to train and evaluate different CNN architectures on a big dataset of lung CT images. Architecture [(TODO) link] Add dropout, experiments with network designs and hyperparameters (layer size, filter sizes & activations ReLu or PReLU Conclusions [same recommendations?] (Previous fin data discussion)] ((Same records for whatever it's worth)) We will use data augmentation to try and make our models a bit more general. Once we get the best CNN model, we can assess its capability to identify lung cancer images earlier than other state-of-the-art methods and present our results by comparing them with them on a separate testing dataset. The project can potentially improve diagnostic tests for early lung cancer detection, thus improving patient survival.