A Convolutional Neural Network (CNN)- based Approach for Lung Cancer Detection Leveraging Classical Edge Detection Techniques: An AI-based Retrospective Study

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

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.

Deep Learning-Based Survival Prediction for Breast Cancer Using Whole Slide Histopathology Images

Breast cancer is currently one of the leading malignancies and mortality rates among women globally, creating an urgent need for accurate and efficient automated diagnostic tools. This study proposes and systematically compares convolutional neural network (CNN) architectures, including VG16, ResNet18, ResNet50, DenseNet121, EfficientNet-B0, and Swin-Transformer, applied to whole slide image (WSI) histopathology data for breast cancer prediction and classification. The models were trained and evaluated on the same WSI dataset with consistent image preprocessing techniques, allowing for objective comparison of performance. Experimental results showed that the performance of the models varied depending on the training strategy. In the non-fine-tune setting, ResNet18 achieved the best results with a C-index of 0.6587 and a Mean time-dependent AUC of 0.7317. When performing fine-tuning, ResNet50 outperformed the other architectures with a C-index of 0.6877 and a Mean AUC of 0.7738. Meanwhile, in the non-fine-tuned setup with combined clinical testing, Swin-Transformer achieved the highest performance with a C-index of 0.6793 and a Mean AUC of 0.7619.

Explainable and Efficient AI-Based System for Automated Breast Cancer Diagnosis Using EfficientNet-B3, CBAM, and Grad-CAM

Breast cancer is one of the leading causes of cancerrelated deaths among women worldwide, making early and accurate diagnosis essential for improving patient survival and treatment outcomes. Traditional histopathological examination is considered the gold standard for breast cancer diagnosis; however, manual analysis is time-consuming, labor-intensive, and subject to inter-observer variability. To address these challenges, this paper presents an Explainable and Efficient AI-Based System for Automated Breast Cancer Diagnosis using deep learning. The proposed methodology employs the BreakHis histopathological image dataset, where images are preprocessed through resizing and normalization before being analyzed using a fine-tuned EfficientNet-B3 model integrated with a Convolutional Block Attention Module (CBAM) to enhance feature representation. Gradient-weighted Class Activation Mapping (Grad-CAM) is incorporated to provide visual explanations of the model’s predictions, improving transparency and interpretability. The trained model achieved a classification accuracy of 97.47% on the test dataset, demonstrating high performance in distinguishing benign and malignant breast cancer images. Furthermore, a Streamlitbased web application enables real-time image upload, prediction, confidence estimation, and explainability visualization. The proposed system offers an efficient, accurate, and interpretable decision-support tool that has the potential to assist pathologists in clinical breast cancer diagnosis while promoting trust in AIdriven healthcare applications.

Deep Learning-Based Segmentation, Classification, and Explainable Diagnosis of Breast Cancer Lesions

Breast cancer remains one of the most prevalent malignancies affecting women worldwide, and early, accurate detection through medical imaging is central to improving survival outcomes. This paper presents an end-to-end computer-aided diagnosis (CAD) framework that performs lesion segmentation, region-of-interest (ROI) extraction, tumor stage estimation, benign/malignant classification, and visual explainability across two complementary imaging modalities: digital mammography, represented by the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM), and breast ultrasound, represented by the Breast Ultrasound Images (BUSI) dataset. For each modality, a modality-specific U-Net architecture was trained to generate binary lesion masks, from which tumor coverage percentage and a rule-based disease stage were derived. Three classifier backbones were benchmarked per modality — ResNet50, VGG16, and a custom convolutional neural network (CNN) for CBIS-DDSM (benign vs. malignant), and ResNet50, EfficientNet-B0, and a custom CNN for BUSI (benign vs. malignant vs. normal) — with Gradient-weighted Class Activation Mapping (Grad-CAM) applied to the strongest classifiers for interpretability. The trained pipelines were deployed in a Django-based web application allowing end users to upload an image and receive segmentation, ROI, stage, classification, confidence, and Grad-CAM outputs in real time. Results show a pronounced modality gap: the U-Net segmentation network achieved a validation Dice coefficient of 0.462 on BUSI versus 0.247 on CBIS-DDSM, and the best classifier reached 84.07% test accuracy on BUSI (ResNet50) compared with 61.86% on CBIS-DDSM (ResNet50), with markedly lower malignant-class recall on mammography (37%) than on ultrasound (76%). These findings quantify how imaging modality, lesion conspicuity, and dataset scale materially affect both segmentation and diagnostic performance, and they underscore the necessity of transparent, modality-aware reporting — including recall/sensitivity for the malignant class, not just aggregate accuracy — when evaluating deep learning systems intended to support breast cancer screening.

An ensemble deep learning model for automated classification of breast cancer from histopathology images