Artificial Intelligence and Machine Learning Based Lung Cancer Detection with Image Processing

Abstract

Radiologists have a challenging and time-consuming task when looking for potentially cancerous lung nodules utilizing computed tomography (CT) imaging. In an increasing number of industries, deep learning algorithms have outperformed conventional techniques with amazing outcomes in recent years. It has been suggested that doctors could treat accidental along with PNs(Pulmonary Nodules) discovered in scanning with the assistance of machine learning(ML) built prediction models for LC (Lung cancer). The incidence and death rate of LC are relatively high because of the high false prediction rate arises due to the main reasons like less Accuracy, by neglecting minimal malignant nodule sizes and wrong diagnoses. Apparently most of the LC screening is based only on the CT which is an invasive process whereas extremely less screening is done using LOW DOSE COMPUTED TOMOGRAPHY (LDCT) which is done noninvasively and linked by means of peculiar advantages over traditional CT. Making better judgments during lung cancer screening may be aided by models of malignancy prediction based on imaging data from LDCT and participant-related factors, especially on the diagnosis and management of nodules. newline