Lung Carcinoma Detection from CT Images Using Image Segmentation

Abstract

Nowadays, researchers experience a severe challenge in detecting lung infection automatically. Lung carcinoma is the most common cancer in which aberrant cells develop and form a malignant tumor that causes death worldwide. During the primary stages of cancer detection and therapy, image processing technologies are often used to increase image quality. Because noise signals are mixed in with creative signals during the image capture process, the quality of the image can be distorted, resulting in poor performance. The pre-processing stage for lung cancer has become crucial, and image denoising is a crucial step that reduces noise. This research work focuses on improving the quality of lung images and diagnosing lung cancer by eliminating misdiagnosis. The images are taken from the Cancer Imaging Archive (CIA) dataset, and noise is removed by utilizing the median filter technique, which successfully eliminates noise from the image while also improving the image quality. The impacted region yields a variety of spectral characteristics. These are examined by using an enhanced watershed and then CNN is applied for the proper classification of the tumor as Malignant or Benign. .