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Performance Analysis of Segmentor Adversarial Network (SegAN) on Bio-Medical Images for Image Segmentation

Publication Type : Book Chapter

Publisher : Advances in Automation, Signal Processing, Instrumentation,

Source : Advances in Automation, Signal Processing, Instrumentation, and Control, p.751-758 (2021)

Url : https://www.researchgate.net/publication/349816266_Performance_Analysis_of_Segmentor_Adversarial_Network_SegAN_on_Bio-Medical_Images_for_Image_Segmentation

ISBN : 9789811582202

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2021

Abstract : Cancer is termed as one of the deadliest disease, and it is becoming a major health problem in the world. This deadly disease can be cured, if it is found at earlier stages. Medical imaging plays an important role in finding this type of diseases and helps in treatment planning. Automated lesion/tumor segmentation is an important and challenging clinical diagnostic task because of tumor’s different shape, volume, contrast, and locations. Since deep learning is promising in many applications, thus motivating us to apply in this important task. In this paper, we propose to do automated tumor segmentation with two challenge dataset named as BraTs 2017 brain tumor and ISIC 2018 skin lesion by using Segmentor Adversarial Network (SegAN), inspired from classical GAN.

Cite this Research Publication : S. Saj T. K, Vishvanathan, S., and Dr. Soman K. P., “Performance Analysis of Segmentor Adversarial Network (SegAN) on Bio-Medical Images for Image Segmentation”, in Advances in Automation, Signal Processing, Instrumentation, and Control, 2021, pp. 751-758.

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