Autor/Autorin: S. POONGUZHALI
Mrs. Poonguzhali S. is working as Assistant Professor in the School of Electrical & Electronics at Sathyabama Institute of Science and Technology. She has published her research papers in many scopus, sci and web of science indexed journals. Her field of interest includes Wireless Sensor Networks, Embedded processors, Medical Electronics.
Lung diseases are the maladies that affect the lungs, the organs that allow us to breathe and it is the most common medical conditions worldwide especially in India. The diseases such as pleural effusion and normal lung are detected and classified in this work. This paper introduces a computer aided classification method in Computer Tomography (CT) Images of lungs created utilizing ANN-BPN. The purpose of the work is to detect and classify the lung diseases by effective feature extraction through Dual-Tree Complex Wavelet Transform [9] and GLCM Features. CT Images are used to segment the entire lung and the parameters are calculated from the segmented image. The parameters are calculated using GLCM. We Propose and evaluate the ANN-Back Propagation Network designed for classification of ILD patterns. The parameters give the maximum classification Accuracy. After result we propose the Fuzzy clustering to segment the lesion part from abnormal lung.
S., POONGUZHALI
Mrs. Poonguzhali S. is working as Assistant Professor in the School of Electrical & Electronics at Sathyabama Institute of Science and Technology. She has published her research papers in many scopus, sci and web of science indexed journals. Her field of interest includes Wireless Sensor Networks, Embedded processors, Medical Electronics.
Über den Autor
La Sra. S. Poonguzhali se incorporó a la Universidad de Sathyabama como profesora asistente en el año 2013. Su campo de interés incluye microprocesadores y microcontroladores, dispositivos electrónicos y electrónica médica. Está cursando su doctorado desde 2016 y su actual área de interés de investigación es el desarrollo de una técnica inteligente para la telemedicina.
Klappentext
Lung diseases are the maladies that affect the lungs, the organs that allow us to breathe and it is the most common medical conditions worldwide especially in India. The diseases such as pleural effusion and normal lung are detected and classified in this work. This paper introduces a computer aided classification method in Computer Tomography (CT) Images of lungs created utilizing ANN-BPN. The purpose of the work is to detect and classify the lung diseases by effective feature extraction through Dual-Tree Complex Wavelet Transform [9] and GLCM Features. CT Images are used to segment the entire lung and the parameters are calculated from the segmented image. The parameters are calculated using GLCM. We Propose and evaluate the ANN-Back Propagation Network designed for classification of ILD patterns. The parameters give the maximum classification Accuracy. After result we propose the Fuzzy clustering to segment the lesion part from abnormal lung.
