Review of Lung Cancer Detection and Classification using Deep Learning

Özet

Lung cancer is a prevalent and deadly disease, and early detection is crucial in improving patient outcomes. Deep learning, a subfield of artificial intelligence, has emerged as a powerful tool for medical image analysis. This research paper comprehensively reviews recent advancements in lung cancer detection and classification using deep learning techniques. The paper begins by highlighting the significance of early detection in lung cancer and the role of deep learning in medical image analysis. The paper's objectives are then outlined, which include discussing deep learning architectures, datasets, preprocessing techniques, and evaluation metrics commonly employed in the field. Various deep learning architectures applicable to lung cancer detection are explored, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Convolutional Recurrent Neural Networks (CRNNs), Generative Adversarial Networks (GANs), and Transfer Learning with pretrained models. Additionally, the paper delves into datasets commonly used in lung cancer research and the preprocessing techniques employed to enhance model performance. Special attention is given to handling class imbalance and extracting the Region of Interest (ROI) from lung images. The research paper also covers different lung cancer detection and classification methods, including nodule detection, nodule classification, malignancy prediction, and multiclass classification. Furthermore, it explores performance evaluation metrics such as sensitivity, specificity, accuracy, Receiver Operating Characteristic (ROC) analysis, precision, recall, F1-score, and Area under the curve (AUC). The challenges and limitations faced in the field, such as limited annotated datasets, uncertainty estimation, generalizability, and ethical considerations, are also discussed. Finally, the paper highlights future directions, including ensemble models, multimodal approaches, explainable AI, integration with other clinical data, and prospects for real-time diagnosis. Overall, this comprehensive review aims to inspire further research and development in lung cancer detection and classification using deep learning, aiming to improve accuracy and efficiency in lung cancer diagnosis.