Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown great potential in improving the accuracy and reliability of nodule detection and classification. This study aimed to develop and evaluate an automatic method for lung nodule detection and classification using a CNN-based architecture applied to computed tomography images from the publicly available LIDC-IDRI database. The proposed method consists of five main steps: image preprocessing, lung parenchyma segmentation using Otsu's thresholding and morphological operations, detection of nodule candidates, feature extraction, and classification using a CNN model. ConclusionsThe proposed CNN-based system demonstrates the feasibility and robustness of deep learning for automatic lung nodule detection and classification. Future work will focus on validating the model across other datasets (e.g., ELCAP, NELSON) and optimizing multi-class classification performance to enhance generalizability and clinical applicability.

Source: CNN February 16, 2026 17:37 UTC