Utilization of deep learning in early detection of lung cancer through CT Scan images: thematic analysis in medical diagnosis innovation

Millie Bright; Pernille Harder

Authors

Millie Bright Universitas East Anglia, United Kingdom
Pernille Harder University of East Anglia, United Kingdom

Keywords:

Deep Learning, CT scan, Lung Cancer, Early Detection, Medical Diagnosis, Convolutional Neural Network

Abstract

Early detection of lung cancer is a major challenge in the global healthcare system, particularly due to the often asymptomatic nature of the disease in its early stages. CT imaging (Computed Tomography) has become a key tool in early diagnosis, but it still faces challenges in terms of accuracy, interpretation time, and reliance on radiological expertise. This study aims to analyze the use of deep learning technology in increasing the effectiveness of lung cancer detection through CT scan images. This study uses a systematic literature review approach and thematic analysis of a number of popular deep learning models such as CNN (Convolutional Neural Network), U-Net, and ResNet. The results show that deep learning can significantly improve the sensitivity and specificity of diagnosis, while speeding up the detection process. The implications of this study open up great opportunities for the transformation of cancer diagnostic systems that are more efficient, accurate, and affordable.

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