Efficientnet-B0-Based Deep Learning Framework for Automated Lung Cancer Detection Using Chest Computed Tomography Images

Abstract

However, early-stage lung cancer is one of the major causes of death from cancer due to its symptoms being absent or hard to detect by conventional clinical analysis. The late diagnosis of this disease causes the effectiveness of the treatment to become poor and low survival rates. Traditional interpretation of medical imaging involves manual analysis which makes it burdensome, prone to variation between observers, and inefficient for screening in cases where many chest CT images need to be analyzed. This calls for efficient computer-based intelligent diagnostics systems that can provide effective, reliable, and fast classification results. In this paper, an EfficientNet-B0-based deep learning model for automated lung cancer classification using chest CT images is proposed. Preprocessing steps such as normalization, lung segmentation, resizing, and data augmentation are used. The custom dataset, specifically PulmoVision Lung Cancer CT Dataset (PLCD-2026), contains balanced image samples that correspond to healthy lungs, benign nodules, and malignancies in order to provide sufficient conditions for effective model training. EfficientNet-B0 learns discriminative anatomical and pathological features and remains computationally efficient by means of optimized network scaling. High classification capabilities of the developed approach can be proved by means of the evaluation procedure conducted in terms of accuracy, precision, recall, and F1-score, which proves reliable identification of different pulmonary diseases. Grad-based visualization is additional confirmation of proper localization of suspicious areas, contributing to the interpretability of the classification process. The developed framework provides an efficient tool for lung cancer detection, helps doctors to make decisions, saves their time, and ensures consistent clinical assessment.