Enhancing Lung Cancer Screening With an Automated Deep Learning System: A Resource‐Efficient Approach

ABSTRACT Lung cancer significantly contributes to cancer mortality worldwide, and prompt diagnosis is essential for enhancing patient outcomes. Identifying the condition at an early stage remains challenging, particularly in regions with limited medical facilities and experienced radiologists. The paper aims to introduce a fully automated DL system capable of identifying, segmenting, and classifying lung cancer at an early phase. This approach aims to enhance the precision and efficacy of lung cancer screening in resource‐constrained environments. The recommended architecture has three stages: (1) lung preprocessing, employing a bilateral filter to enhance image quality; (2) lung segmentation, utilizing Otsu's thresholding to delineate lung areas; and (3) lung cancer classification, implementing a modified CNN referred to as the P‐Model. The framework was assessed utilizing the publicly available IQ‐OTH/NCCD dataset. The proposed framework exhibited high performance metrics, with lung segmentation accuracy at 96%, classification precision at 97%, sensitivity at 96%, and an F1 score of 96%. Additionally, the Matthews correlation coefficient was recorded at 0.9406. The findings indicate that our paradigm surpasses previous studies across all pertinent evaluation metrics. The proposed deep learning approach shows considerable potential for precise detection and classification of lung cancer, particularly in areas with limited resources. This study significantly advances the field of lung cancer detection and has the capacity to enhance healthcare outcomes and patient care standards globally.