Exploring 3D Convolutional Neural Networks for Enhanced Detection in Lung Cancer Classification

Accurate lung cancer diagnosis is crucial for timely treatment and improved patient outcomes. However, the limitations associated with traditional manual interpretation techniques often result in misdiagnosis, emphasizing the pressing need to develop an automated tool. This study proposes a groundbreaking approach, utilizing a 3D convolutional neural network (CNN) model for early detection. By analyzing 3D data from CT scans, the specialized 3D CNN demonstrates a high level of accuracy in detecting abnormal lung tissue growth by effectively extracting intricate features. By investigating two different 3D CNN architectures, incorporating the early stopping technique, and examining the impact of parallelism on accuracy and training time, the study identifies Model B with a batch size of 2, achieving 100% accuracy. In contrast, parallelism implementation reduces training time by 96.43%. By training the model with labeled CT scan data, it becomes proficient in recognizing patterns indicative of lung cancer, revolutionizing early detection and providing a reliable tool for healthcare professionals. This advancement holds immense potential for enabling timely interventions and formulating appropriate treatment plans, ultimately playing a critical role in potentially saving lives.