Harnessing Deep Learning for Lung Cancer Early Detection through AI-Enabled Chest CT Imaging Analysis

Lung illnesses comprise a spectrum of ailments that affect the ability to breathe with lung cancer standing as a significant contributor to global mortality rates. Timely detection substantially improves survival prospects, with average survival rates increasing from 14 to 49 percent when diagnosed early. While CT (Computerised Tomography) is more efficient than traditional Radiography, comprehensivediagnosis often involves employing multiple imaging modalities to complement one another. Deep neural networks have been developed recently and assessed in terms of using CT scans to identify lung tumors. CT images of the chest are increasingly pivotal in diagnosing and managing lung cancer clinically. Artificial intelligence (AI) holds promise in swiftly assessing CTscans to differentiate cancerous findings from other clinical conditions. Scan results from emergency room, oncology, and pneumonia cases are included in normal control scans. Among 140 patients with laboratory- confirmed pneumonia unrelated tomalignancy, the false positive rate was 10%.AI-driven algorithms demonstrate the capability to identify CT scans indicative of lung cancer-associated pneumonia and to distinguish this pneumonia with high specificity across various patient demographics. Lung cancer, although not contagious, can have fatal consequences when affecting the lungs. A proposed technique for detecting lung infections aims to discern whether an individual is afflicted with cancer and assess the extent of infection, facilitating early intervention to potentially save lives.