Deep Learning-Based Lung Cancer Segmentation and Volumetric Analysis Using CT Imaging: A Comprehensive Survey.

Lung cancer detection in CT scans using extracted nodule features and precise multistage segmentation with deep learning based ensemble model

Lung cancer is one of the deadliest malignancies, known for rapid growth and has high potential of spreading the original tumor cells to other cells of the body. According to GLOBOCAN 2020, there were 2.2 million new lung cancer cases and 1.8 million deaths, making up 18% of global cancer deaths. The American Lung Association (ALA) reports that only 25.8% of cases are detected early, with a 5-year survival rate. Early detection is crucial but remains difficult due to nonspecific symptoms and current imaging limitations. This study proposes a deep Convolutional Neural Network (CNN) approach to improve lung cancer detection by facilitating accurate, automated diagnosis of lung nodules in early stage cancer. We combine two advanced deep learning models, VGG16 and U-Net++, to enhance the classification of medical images. The approach leverages VGG16’s strengths in feature extraction and U-Net++’s robust multiscale processing to handle the diverse shapes and sizes of tumors in lung tissue. These models are ensembled to advance our capabilities in processing medical images. Features extracted from both models are concatenated to create a comprehensive and flexible representation of the input data. The feature set is flattened and then undergoes further processing using Dense layers, Batch Normalization, and Dropout layers to improve generalization and prevent overfitting. The final output layer classifies the images as ‘Benign,’ ‘Malignant,’ or ‘Normal.’ Extensive experiments on a lung image dataset demonstrate significant classification improvements over individual models. Performance evaluation using metrics such as accuracy, precision, recall, and F1-score shows higher results for the ensemble model. This study concludes that integrating VGG16 and U-Net + + into this CNN architecture can significantly enhance lung cancer detection performance by achieving a remarkable accuracy of 96% and provide a reliable tool for clinicians in early stage diagnosis and treatment monitoring. Our proposed brings improvement in accuracy, segmentation and feature extraction and thereby proves to be one among the top models available.

Multi-class lung cancer detection and classification from CT scans using CNN transformer

AI-Driven Tumor Characterization and Histological Subtype Classification in Lung Cancer Using CT Imaging

Background/Objectives: Lung cancer is still one of the top cancer mortality causes around the world, and there is a need for an accurate and clinically reliable diagnostic tool. While Computed Tomography (CT) imaging is very useful for evaluation of pulmonary nodules and tumor morphology, its interpretation is complicated by inter-patient variability, imaging artifacts, low tissue contrast, and tumor heterogeneity. Although Computer-Aided Diagnosis (CAD) systems have enhanced the diagnostic process, handcrafted feature-based approaches often fail to capture complex tumor characteristics, and numerous deep learning systems lack clinical interpretability. To tackle these challenges, this study suggests a unified diagnostic approach to characterize the tumor comprehensively. Methods: Lung window intensity clipping and the MedSAM foundation model are used to segment the tumor regions. After segmentation, handcrafted texture, shape, morphology and keypoint features are extracted in addition to deep features extracted by ResNet50. Particle Swarm Optimization (PSO) is used to select and refine the features, followed by an LSTM network that learns the sequential relationships among features for histological subtype classification. Results: It was observed that the proposed approach outperformed the benchmark approaches by attaining a higher accuracy of 93.70% and 94.70% on the Lung-PET-CT-Dx and LIDC-IDRI datasets, respectively. The ablation analysis supports the contribution of each module, clearly showing the progressive improvement of the overall classification performance obtained by integrating the complementary modules. Conclusions: The proposed framework effectively incorporated MedSAM-based tumor segmentation, radiomic feature analysis, and deep feature representation and sequential dependency modeling all in a single diagnostic workflow for lung cancer evaluation and diagnosis. These results prove its feasibility for explainable computer-aided diagnosis and decision support for lung cancer evaluation.

A new approach for lung cancer diagnosis: parallel feature learning

Lung cancer, one of the most common types of cancer worldwide, can be fatal. Early diagnosis saves lives. Computed tomography (CT) is used in the diagnosis of the disease. Since the radiology specialist evaluates this X-ray result, the specialist's interpretation can vary. Furthermore, the analysis by the radiologist is both time-consuming and costly. However, a cancer diagnosis approach based on deep learning models supports the radiologist's decision. In this study, a parallel feature learning architecture developed for lung CT images was designed. This architecture focuses on learning different features from each parallel path by using deformable and dilated convolution layers together. Dilated convolution captures semantic features in images with different dilated rates ratios by expanding receptive field, while deformable convolution better captures structural changes. This mechanism allows for more flexible and distinctive feature extraction without significantly increasing computational complexity. The proposed architecture was tested on three different lung cancer datasets: the Public Lung Cancer Dataset, IQ-OTH/NCCD, and LIDC-IDRI. Experimental findings demonstrate robust and consistent classification performance, achieving accuracy rates of 99.44%, 98.75%, and 98.62%, respectively. This shows that the proposed architecture offers a reliable solution for lung cancer diagnosis.

Deep learning driven early detection of lung cancer from CT images using transfer learning approaches

The proposed system demonstrates potential as an assistive tool for automated lung cancer screening, warranting further validation on larger and multi-institutional datasets before clinical application.

First steps in clinical implementation of a lung nodule classification method by artificial intelligence

This work represents an initial translational step toward real-world clinical implementation of AI-based lung nodule classification at Cheikh Zaid Hospital and highlights the feasibility of integrating AI tools into routine radiology workflows.