Related Experiment Video For Adaptive Mode Ostu Binarization (AMOB)
Updated: Aug 14, 2025
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
An automatic classification of pulmonary nodules for lung cancer diagnosis using novel LLXcepNN classifier
Vijay Kumar Gugulothu1, Savadam Balaji2
1Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Deemed to be University and Govt. Polytechnic, Masab Tank, Hyderabad, Telangana, India. vijaykumargugulothu@klh.edu.in.
Introduction:
A critical step to ameliorate diagnosis and extend patient survival is Benign-malignant Pulmonary Nodule (PN) classification at earlier detection. On account of the noise of Computed Tomography (CT) images, the prevailing Lung Nodule (LN) detection techniques exhibit broad variation in accurate prediction.
Methods:
Thus, a novel Nodule Detection along with Classification algorithm for early diagnosis of Lung Cancer (LC) has been proposed. Initially, employing the Adaptive Mode Ostu Binarization (AMOB) technique, the Lung Volumes (LVs) isextortedas of the image together with the extracted lung regions is pre-processed. Then, detection of LNs takes place, and utilizing Geodesic Fuzzy C-Means Clustering (GFCM) Segmentation Algorithm, it is segmented.Next, the vital features are extracted, and the Nodules are classified by utilizing Logarithmic Layer Xception Neural Network (LLXcepNN) Classifier grounded on the extracted feature.
Results:
The nodules are classified as Benign Nodules (BN) and Malignant Nodules (MN) by the proposed classifier. Lastly, the Lung CT images are scrutinized.
Discussion:
Thus, when weighed against the prevailing techniques, the proposed systems' acquired outcomes exhibit that the rate of accuracy of classification is enhanced.
