Novel Technology for Lung Tumor Detection Using Nanoimage

Abstract

Nowadays, nanotechnology is gaining more advantages and widely used in many real-life applications including minute tumor detection and effective diagnosis. Nanoscale imaging technique significantly increases the precision, accuracy of tumor detection, and classification of tumor into benign and malignant. Computerized tomography (CT) scan is important for lung cancer diagnosis and research, because it gives accurate segmentation results in lung tumor. In the proposed NBDS method, nanotechnology based detection scheme is used to detect the lung tumors in nanoscale range. Pulmonary nodules are symptoms of lung cancer. The shape and size of these pulmonary nodules are used to diagnose lung cancer in CT images. In the early stages, nodules are very small, and radiologist has to refer to many CT images to diagnose the disease, causing operator mistakes. Image processing algorithms are used as an aid to detect and localize nodules. Here, the input nanoimage is enhanced by using the technique of unsharp masking with the anisotropic filter. By using toboggan algorithm, lung cancer images are segmented. Nano measuring tool for graphical user interface is developed in MATLAB software to detect the lung tumor area or lung lesion in the body (in nanometers). Image classification and feature extraction are done by K-nearest neighbor (KNN) and support vector machine (SVM) with Bag of Visual Words (BoVW) classifiers. The overall accuracy of 97% is obtained using GLCM and FOS features in MATLAB software. The convolutional neural network (CNN) classifier gives the maximum accuracy of 98.19% in MATLAB 2017a software, and hence, this classifier does not need the feature extraction step. Furthermore, the average time consumption for one lesion segmentation was under 4 s using our proposed method. The self-assembled biocompatible nano technique is created to detect the tumor area (in nanometers), and it automatically evaluates the disease. This novel nanotechnology-based tumor area detection schemes achieve robust, efficient, and accurate lung lesion segmentation in nano CT images.

KEYWORDS:

• Lung cancer
• Anisotropic filter
• Feature extraction
• KNN
• FFNN
• CNN
• SVM with BoVW classifier
• Toboggan algorithm
• Nano image

ACKNOWLEDGEMENTS

The authors thank the management of Arunachala College of Engineering for Women for their continuous support and encouragement throughout the work. The authors also acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health for their critical roles in the creation of the free, public available LIDC-IDRI database used in this study. Finally, the authors thank the anonymous reviewers for helping to strengthen this article.

Additional information

Notes on contributors

K. Vijila Rani

K Vijila Rani is a research scholar of Arunachala College of Engineering for Women. She received her bachelor's degree in electronics and communication engineering from Anna University, Chennai, master's degree in communication system from Anna University, Chennai. She is currently working towards her PhD degree in Arunachala College of Engineering for Women, Vellichanthai at Anna University, Chennai, India. She has already published few international journal and conference papers and participated in some international conferences. Her research interests include medical image processing, nanotechnology, image segmentation methodology, and tumor detection scheme.

S. Joseph Jawhar

S Joseph Jawhar is working as a principal/professor in the Department of Electrical and Electronics Engineering, Arunachala College of Engineering for Women, Vellichanthai. He received his BE degree in EEE from Madurai Kamaraj University, ME degree in applied electronics from Bharathiar University, and PhD in power electronics from Anna University, Chennai. He has already published few international journal and conference papers and participated in some international conferences. His research interests include power electronics, AC drives and control, random PWM, matrix converters. Email: [email protected]