A SYSTEMATIC REVIEW OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN LUNG CANCER DETECTION AND ACCURATE DIAGNOSIS

Abstract

Lung cancer, which has the greatest fatality rate of any cancer kind, is the most serious form of the disease.Many lives may be saved by early detection.Along with breast cancer in women and prostate cancer in men, lung cancer is the second most frequent kind of cancer.According to the International Association of Cancer Society's (IACS) projections, there will be approximately: 131,880 lung cancer fatalities (119,100 in men and 116,660 in women) 235,760 new cases of lung cancer (69,410 in men and 62,470 in women).Due to its tiny size and placement of the glands, lung cancer is asymptomatic in its early stages on a CT scan.Symptoms only arise when the illness is at a more advanced stage.Early detection techniques like computed tomography (CT) and magnetic resonance imaging (MRI) are common medical practices that increase patient survival.Prior intelligent techniques relied on manually created feature extraction techniques like Sequential Flood Feature Selection Algorithms (SFFSA) or Genetic Algorithms (GA), which may assist in producing the best possible features.Deep learning technology has recently been applied in CAD systems to automatically extract picture characteristics, and several medical image processing tools have proven successful as a result.

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