Abstract
BACKGROUND: This paper aimed to enhance the diagnostic process of lung abnormalities in computed tomography (CT) images, particularly in distinguishing cancer cells from normal chest tissue. The rapid and uneven growth of cancer cells, presenting with variable symptoms, necessitates an advanced approach for accurate identification. OBJECTIVE: To develop a dual-sampling network targeting lung infection regions to address the diagnostic challenge. The network was designed to adapt to the uneven distribution of infection areas, which could be predominantly minor or major in different regions. METHODS: A total of 150 CT images were analyzed using the dual-sampling network. Two sampling approaches were compared: the proposed dual-sampling technique and a uniform sampling method. RESULTS: The dual-sampling network demonstrated superior performance in detecting lung abnormalities compared to uniform sampling. The uniform sampling method, the network results: an F1-Score of 94.2%, accuracy of 94.5%, sensitivity of 93.5%, specificity of 95.4%, and an area under the curve (AUC) of 98.4%. However, with the proposed dual-sampling method, the network reached an F1-score of 94.9%, accuracy of 95.2%, specificity of 96.1%, sensitivity of 94.2%, and an AUC of 95.5%. CONCLUSION: This study suggests that the proposed dual-sampling network significantly improves the precision of lung abnormality diagnosis in CT images. This advancement has the potential to aid radiologists in making more accurate diagnoses, ultimately benefiting patient treatment and contributing to better overall population health. The efficiency and effectiveness of the dual-sampling approach in managing the uneven distribution of lung infection areas are key to its success.