V. Deepa

Work place: Department of Computer Science & Engineering, SRM Institute of Science and Technology Vadapalani, Chennai, Tamil Nadu, India

E-mail: dv1019@srmist.edu.in

Website: https://orcid.org/0000-0003-3758-7725

Research Interests: Computational Learning Theory, Image Processing


V. Deepa received B.E degreein CSE from Velammal Engineering College, affiliated to Madras University, Tamil nadu in 2001. M.E Degree in CSE from Sri Venkateshwara college of Engg, Affliated to Anna University, Chennai in 2005.He is currently working toward the Ph.D.degree at the Department of Computer Science & Engineering SRM University ,Vadapalanl Chennai , India. His research interestsinclude Image Processing ,Machine Learning and fundamentals of Python.

Author Articles
Deep-ShrimpNet fostered Lung Cancer Classification from CT Images

By V. Deepa Mohamed Fathimal. P

DOI: https://doi.org/10.5815/ijigsp.2023.04.05, Pub. Date: 8 Aug. 2023

Lung cancer affects the majority of people, due to genetic changes in lung tissues. Several existing methods on lung cancer detection are utilized with machine learning, but it does not accurately classify the lung cancer and also it takes high computation time. To overwhelm these issues, Deep-ShrimpNet fostered Lung cancer classification from CT images (LCC-Deep-ShrimpNet) is proposed. Initially, the input lung CT images are taken from IQ-OTH/NCCD Lung Cancer Dataset. Then the input lung CT images are pre-processed using Kernel co-relation method. Then these pre-processed lung CT images are given to Bayesian fuzzy clustering for extracting lung nodule region. Then the extracted lung nodule region is given into Deep-ShrimpNet classifier for representing features and classifying the lung CT images as normal (Healthy), Benign, and Malignant. The proposed LCC-Deep-ShrimpNet method is activated in python. The performance of the proposed LCC-Deep-ShrimpNet method attains 26.26%, 16.9%, 12.67%, 21.52% and 24.05% high accuracy, 68.86%, 59.57%, 57%, 62.72% and 65.69% low error rate and 60.76%, 53.67%, 68.58%, 59% and 56.61% low computation time compared with the existing methods.

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