Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification. Issue 4 (2nd April 2020)

• Record Type: Journal Article
• Authors: Rani, K. Vijila; Jawhar, S. Joseph
• Is Part Of: International journal of imaging systems and technology. Volume 30:Issue 4(2020)
• Page Start: 899
• Page End: 915
• Publication Date: 2020-04-02
• Subjects: artificial neural network -- ATMSBR segmentation -- bag of visual words classifier -- bagged random tree classifier and deep neural network (DNN) -- boosted deep convolutional neural network (BDCNN) -- deep convolutional neural networks -- feed forward neural network -- lung cancer -- multiclass support vector machine classifier -- nanoimaging; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22422
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Lung tumor is a complex illness caused by irregular lung cell growth. Earlier tumor detection is a key factor in effective treatment planning. When assessing the lung computed tomography, the doctor has many difficulties when determining the precise tumor boundaries. By offering the radiologist a second opinion and helping to improve the sensitivity and accuracy of tumor detection, the use of computer‐aided diagnosis could be near as effective. In this research article, the proposed Lung Tumor Detection Algorithm consists of four phases: image acquisition, preprocessing, segmentation, and classification. The Advance Target Map Superpixel‐based Region Segmentation Algorithm is proposed for segmentation purposes, and then the tumor region is measured using the nanoimaging theory. Using the concept of boosted deep convolutional neural network yields 97.3% precision, image recognition can be achieved. In the types of literature with the current method, which shows the study's proposed efficacy, the implementation of the proposed approach is found dramatically.