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

CT Imaging-based Ensemble Deep Learning Model for Lung Cancer Detection

Lung cancer is the main cause of death related to cancer globally, taking the lives of about 1.8 million people each year. Detecting it early and accurately from CT scan images is very important for better patient results. Three sophisticated Convolutional Neural Network (CNN) models—ResNet50, DenseNet121, and EfficientNetB0—are used in this comprehensive study to automatically classify lung CT scan images into two categories (benign and malignant). The IQ-OTH/NCCD Lung Cancer Dataset, which is openly accessible on Kaggle, was used to train and evaluate the models. This dataset was created by Aditya Mahimkar and contains 1300 CT scan slices from 110 different patient cases. After preprocessing, only benign and malignant CT scan images were used for binary classification. To evaluate the performance of each model following training through transfer learning and fine-tuning, metrics such as accuracy, precision, recall, F1-score, and the confusion matrix were employed. ResNet50 outperformed DenseNet121 and EfficientNetB0 in terms of accuracy. A combined Weighted Ensemble Model that uses the probability outputs from all three networks was also created, which improved the overall classification performance and how well the model works in different situations. This study shows that using deep learning and combining models can be very helpful tools in detecting lung cancer during clinical screenings.

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

Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology

Lung cancer remains the leading cause of cancer-related mortality worldwide, while histopathological diagnosis is often affected by inter-observer variability and the substantial workload associated with manual slide examination. Although deep learning has shown considerable potential in computational pathology, comprehensive benchmarks that integrate tissue classification and region segmentation within a unified analytical framework remain limited. This study presents a two-stage deep learning framework for multi-class tissue classification and pixel-level histopathological region segmentation, accompanied by a systematic comparison of state-of-the-art architectures at each stage. For tissue classification, six models, a custom convolutional neural network, VGG16, DenseNet, MobileNetV3, a custom Vision Transformer, and YOLO11, are evaluated on a combined dataset of 39,000 images derived from LC25000 and LungHist700. The models distinguish between adenocarcinoma, squamous cell carcinoma, and normal lung tissue. YOLO11 achieves the best classification performance, with an accuracy of 98.38%, a five-fold cross-validation accuracy of 98.21 +/- 0.35%, and a macro F1-score of 0.98. For region segmentation, U-Net, ResNet-encoder U-Net, DeepLabV3+, and YOLO11-seg are evaluated using the GlaS gland segmentation benchmark. DeepLabV3+ obtains the highest Intersection over Union of 0.80 and a Dice score of 0.89, while YOLO11-seg achieves a comparable Intersection over Union of 0.79 using approximately 14x fewer parameters. The best-performing classification and segmentation models are subsequently integrated into an end-to-end framework, providing an accurate, computationally efficient, and reproducible baseline for automated histopathological image analysis.

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.

CNN-RNN framework for lung cancer classification using CT imaging and GAN-based augmentation

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, and early identification of malignant abnormalities plays an important role in improving patient survival rates. However, accurate lung cancer classification using CT imaging remains challenging because of limited dataset availability, class imbalance, overlapping lesion characteristics, and lack of interpretability in existing deep learning systems. This study presents a GenAI-driven CNN–RNN framework for explainable lung cancer classification using CT imaging and GAN-based augmentation. The proposed framework integrates convolutional neural networks for spatial feature extraction, LSTM-based recurrent learning for sequential dependency analysis, GAN-assisted augmentation for improving minority class representation, and attention-guided feature fusion for enhanced classification performance. The experimental evaluation was conducted using the publicly available IQ-OTH/NCCD lung cancer CT imaging dataset containing Normal, Benign, and Malignant categories. During preprocessing, normalization, resizing, and image enhancement operations were applied to improve image consistency before training. The framework was trained using the Adam optimizer with 50 epochs and evaluated using 5-fold cross-validation. Experimental results demonstrated that the proposed framework achieved an accuracy of 92.84%, precision of 91.76%, recall of 90.42%, F1-score of 91.08%, and ROC-AUC value of 0.94. Grad-CAM and SHAP visualization methods further improved interpretability by highlighting important lesion regions influencing prediction outcomes. The obtained findings suggest that the proposed CNN–RNN framework can support CT image-based lung cancer classification under limited dataset conditions for CT image-based lung cancer classification under limited medical imaging conditions.

Lung Cancer Classification from CT Images Using Ensemble Transfer Learning of VGG16, ResNet50V2 and MobileNetV2

Lung cancer remains one of the most common cancers worldwide, and how early it is caught shapes what treatment is possible and how long a patient lives. Computed tomography (CT) sits at the center of this process, and deep learning has become a standard tool for reading CT images, with strong results across medical image classification tasks. This paper proposes an ensemble approach that brings pre-trained VGG16, ResNet50V2, and MobileNetV2 together in a single framework, classifying lung CT images as benign, malignant, or normal. The dataset consists of 1946 CT images from 473 patients, collected at Atatürk University Hospital and labeled by a nuclear medicine physician. All three backbones were fully fine-tuned end-to-end, an approach that an ablation study confirmed outperformed both fully frozen and partially frozen configurations. Splits were made at the patient level throughout, so no patient appears in both training and validation. Five-fold cross-validation gave 89.42 ± 3.21% accuracy and a macro F1 of 0.865 ± 0.036; the same configuration, retrained on a separate patient-level split, reached 90.62% accuracy with a macro F1 of 0.874. The results indicate that ensemble transfer learning can serve as a supporting tool in lung cancer screening.