A multilevel self‐attention based segmentation and classification technique using Directional Hexagonal Mixed Pattern algorithm for lung nodule detection in thoracic CT image. Issue 5 (4th March 2022)

• Record Type: Journal Article
• Authors: Sahaya Jeniba, J.; Milton, A.
• Is Part Of: International journal of imaging systems and technology. Volume 32:Issue 5(2022)
• Page Start: 1496
• Page End: 1510
• Publication Date: 2022-03-04
• Subjects: classification -- Directional Hexagonal Mixed Pattern -- lung nodules -- multilevel self‐attention and CT images -- segmentation; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22721
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Pulmonic nodules are unusual growing of tissues; originate on one lung or both lungs. They are the round, trifling mass of soft tissues in the lung area. Habitually, pulmonic nodules are indications of lung tumors, but they may be nonthreatening. When identified earlier and treated in time, the patient's life expectancy increases. The anatomy of the lung is highly interconnected in nature, which makes it difficult to diagnose pulmonic nodules by diverse clinical imaging practices. A network model is presented in this paper for accurate classification of pulmonic nodules from computed tomography scans images. The lung images are subjected to semantic segmentation using Attention U‐Net to isolate the pulmonary nodules. The proposed Directional Hexagonal Mixed Pattern is applied to generate a new texture pattern. Then, the nodules are classified by combining the proposed multilevel network model with the self‐attention network. This paper also demonstrates an experimental arrangement called tenfold cross‐validation without a segmentation mask, in which the nodules that had been marked as less than 3 mm by radiologists are discarded. This has obtained an improved result. The experimental results show that with and without segmentation masks the proposed classifier scores an accuracy of 90.48% and 91.83%. In addition, it has efficiently produced the measure of area under curve as 98.08%.