Efficient Deep Lung Diagnostic Network (EDLD-Net) for Optimized Lung Cancer Detection in Lung CT Scans

Abstract

Lung malignancies are primary causes of cancer related deaths worldwide where chances of successful cures will be significantly impacted by early disease identifications. Computed Tomography (CT) is one of the best imaging techniques for identifying lung cancer. The Convolutional Neural Network (CNN) approach is inefficient and takes more time and processing resources for real-time or resource-constrained applications. We suggested the Efficient Deep Lung Diagnostic Network (EDLD-Net) method, which lowers computational complexity, to solve this problem. Otsu thresholding is used to separate areas of interest, Sobel filtering is used to emphasize the boundaries of possible tumor locations, and histogram equalization is used to resize and enhance CT scan images in order to increase contrast and minimize noise. The method reduces data redundancy by using adaptive pooling and the Grey-Level Co-Occurrence Matrix (GLCM) for texture feature extraction. Images are categorized as malignant or non-cancerous using lightweight CNNs that employ Transfer Learning (TL), which improves models’ accuracies while minimizing execution times. Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, a generally established benchmark for lung cancer detections, was utilized to train and validate models where the dataset dramatically reducing execution times for real-time applications while maintaining 99.85% accuracy, 99.75% sensitivity, and 99.80% specificity. These findings show that, especially in clinical settings with limited computational resources, EDLD-Net offers a scalable, cost-effective, and precise method to lung cancer detection.