Image Acquisition and Pre-processing for Detection of Lung Cancer using Neural Network

Lung cancer has a relatively low-down cure speed in its later stage. Lung cancer survival rates might be significantly enhanced if effective early detection could be obtained. Early lung cancer identification is crucial for human health. The suggested technique, which consists of two phases, attempts to identify lung cancer early. It Imports lung CT scans into the structure immediately, and then proceeds to the image layout step, utilizing explicit image management processes. The proposed method incorporates several advances, including image acquisition, pre-processing, binarization, thresholding, division, feature extraction, and neural organization identification. The binarization method changes matched pictures and contrasts them with edge views and, the feature extraction technique eliminates specified essential properties from the segmented images. The brain structure is constructed using the retrieved attributes, and the framework is then scanned for malignant or benign images. The proposed framework produces acceptable results, and the proposed technique has a precision of 94 percent.