Systematic Literature Review of Deep Learning Techniques for Lung Cancer Segmentation

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

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

Lung cancer remains one of the foremost causes of cancer-related mortality worldwide, primarily due to its frequent late-stage diagnosis and the limited efficacy of available treatments at advanced stages. This study seeks to advance early detection through the application of deep learning techniques for the automated classification of lung CT images into benign, malignant, and normal categories. A comprehensive methodological framework was adopted, encompassing data acquisition, preprocessing, model development, training, validation, and performance evaluation, with patient-level data splitting employed to prevent slice-level data leakage. Among six tested architectures (VGG16, Custom CNN, MobileNetV2, ResNet50, InceptionV3, and EfficientNetB0), the fully fine-tuned VGG16 model optimized via transfer learning and trained on the IQ-OTH/NCCD lung cancer dataset exhibited the strongest overall performance, achieving a mean test accuracy of 89.39% ± 2.10% (test loss 0.329) across three independent runs, and 89.70% ± 2.75% under 5-fold patient-grouped cross-validation. One-way ANOVA (F = 17.55, p < 0.0001) followed by Tukey’s HSD post-hoc test confirmed that VGG16 significantly outperformed ResNet50 and EfficientNetB0. To enhance interpretability, Grad-CAM visualizations were generated for all three classes, indicating that the model’s attention broadly corresponded to anatomically relevant lung regions. Trained and evaluated on a Google Colab GPU environment, 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.

Medical image segmentation with optimal learning from limited data and annotations: A comprehensive review

This survey extends beyond traditional and deep learning-based augmentation techniques or deep semi-supervised approaches, by explicitly focusing on medical/clinical imaging modalities, by explicitly focusing on CT, MRI, and X-ray, offering a broader perspective.

Lung Cancer Detection from Histopathological Images using Custom CNN With XAI Techniques

One of the leading causes of cancer-related mortality worldwide is lung cancer. Improving patient survival requires early and precise diagnosis. Recent developments in deep learning make it possible to analyse histopathological lung pictures automatically. Because most deep learning models are opaque and considered black-box systems, their application in clinical practice is constrained. An explainable deep-learning framework for the categorisation of lung cancer is presented in this article. Gradient-weighted Class Activation Mapping (Grad-CAM) visualisation and a custom Convolutional Neural Network are also used. Three classes of histopathological lung tissue images: lung squamous cell carcinoma, lung adenocarcinoma, and normal tissue. Images are scaled to 224*224 pixels and preprocessed using data augmentation and normalisation techniques to enhance generalisation before training. With a test accuracy of 96.62%, the suggested Custom CNN is comparable to existing deep learning models like DenseNet121 (96.6%) and other CNN-based methods documented in earlier research. The suggested approach uses Grad-CAM to provide visual interpretability alongside classification performance.

Automated lung cancer classification using intensity-driven RoI selection and transfer learning.

Lung cancer diagnosis increasingly relies on advanced medical imaging systems and expert interpretation across heterogeneous clinical data sources. However, accurate clinical decision-making remains challenging due to variations in expert judgment and the complexity of extracting discriminative patterns from electronic health records and radiological datasets. To address these challenges, this paper proposes an enhanced lung cancer classification framework that leverages intensity-driven region-of-interest (RoI) selection from publicly available benchmark datasets, including the Lung Image Database Consortium Image Collection (LIDC-IDRI) and The Cancer Imaging Archive (TCIA). The proposed methodology incorporates customized label refinement and precise annotation of vulnerable RoI regions to capture clinically relevant features associated with malignant nodules. A high-dimensional RoI mapping strategy is employed to improve feature representation and discrimination. Furthermore, a feedback-driven optimization mechanism is integrated within a transfer learning framework to iteratively refine model parameters and enhance learning stability. The optimized RoI representations are transferred to customized deep learning models, enabling efficient knowledge reuse and robust decision-making. The proposed approach is implemented using the CoVNet architecture and evaluated under a 60:40 training-testing split. Experimental results demonstrate a classification accuracy of 97.84%, validating the effectiveness of the proposed framework in improving predictive performance for lung cancer classification.

Uncertainty Quantification in Medical Image Segmentation: A Comprehensive Survey

A comprehensive survey of UQ techniques in medical image segmentation is presented, categorizing existing approaches into Bayesian methods, deep ensembles, deterministic methods, test-time data augmentation, and hybrid models, while treating foundation-model-based UQ as a separate cross-cutting category.