PULMONARY LUNG NODULE IDENTIFICATION USING 3D DEEP CNN

Abstract

The main aim of the study is to identify CT Images based pulmonary nodule using 3D Deep Convolutional Neural Network. Many studies have developed novel strategies for detecting PNCs, but the intricacy and diversity of pulmonary nodules make this a hard endeavor. CA detection identifies the location of lesions. This approach is useful for more thorough investigations of medical photographs. Medical photos are employed in scientific research to discover adaption signals. The picture quality may be assessed using the SN (signal to noise) ratio and signal detection in an image. Furthermore, a 3D DCNN classifier was successful in reducing false positives. As a consequence, the suggested model performed well during validation testing on the LUNA 16 dataset. As a result, the suggested model is regarded as an effective clinical tool for screening lung cancer.