Lung Cancer CT scan image for Federated Learning

This dataset contains 3,200 high-quality slice-level lung CT images categorized into three distinct diagnostic classes. It is designed to support machine learning, deep learning, and federated learning research for the automatic detection and classification of lung cancer, contributing to innovations in privacy-preserving medical AI, multi-institutional collaboration, and explainable diagnostic systems. Dataset Composition: The dataset is organized into three distinct classes based on standard radiological features and pathological definitions: ⦁ Malignant (1,310 images): Confirmed carcinomas, such as Adenocarcinoma, typically characterized by irregular margins, nodular densities, and surrounding parenchymal distortion. ⦁ Benign (905 images): Non-cancerous nodules, such as Hamartomas or Granulomas, characterized by circumscribed structures, smooth margins, or calcification. ⦁ Normal (985 images): Scans exhibiting clear lung parenchyma with no evidence of nodules or pathological focal points. Data Preprocessing and Structuring: ⦁ Images are formatted as single-channel (grayscale) CT slices. ⦁ The dataset underwent a standardized preprocessing pipeline where images were resized to 224×224 pixels to ensure uniformity across samples. ⦁ Scans were enhanced via Contrast Limited Adaptive Histogram Equalization (CLAHE) to handle differences in brightness and contrast, and normalized using shared per-channel mean and variance to ensure consistent pixel intensity (range of 0 to 1). Applications: This dataset can be effectively used for: ⦁ Medical image classification and computer-aided oncology diagnostics. ⦁ Development and benchmarking of advanced deep learning architectures (e.g., CNNs, Vision Transformers, and Hybrid models). ⦁ Simulating decentralized, multi-institutional Federated Learning (FL) environments, specifically for testing Non-IID (Independent and Identically Distributed) data partitioning with quantity skew and label heterogeneity. ⦁ Evaluating Explainable AI (XAI) frameworks, such as validating Grad-CAM saliency maps and quantitative trustworthiness metrics like Deletion AUC. File Information: ⦁ Total Images: 3,200 ⦁ Color Space: Grayscale (Single-channel) ⦁ Resolution: 224×224 pixels ⦁ Folder Structure: Each class is stored in a separate labeled directory (Malignant, Benign, Normal).