Version 1
: Received: 15 June 2022 / Approved: 15 June 2022 / Online: 15 June 2022 (09:02:28 CEST)
How to cite:
GN, B. B.; Sirisha, N.; Kezia Rani, B.; Bethu, S. A Frame Work Design of Chicken-Sine Cosine Algorithm-based Deep Belief Network for Lung Nodule Segmentation and Cancer Detection. Preprints2022, 2022060217. https://doi.org/10.20944/preprints202206.0217.v1
GN, B. B.; Sirisha, N.; Kezia Rani, B.; Bethu, S. A Frame Work Design of Chicken-Sine Cosine Algorithm-based Deep Belief Network for Lung Nodule Segmentation and Cancer Detection. Preprints 2022, 2022060217. https://doi.org/10.20944/preprints202206.0217.v1
GN, B. B.; Sirisha, N.; Kezia Rani, B.; Bethu, S. A Frame Work Design of Chicken-Sine Cosine Algorithm-based Deep Belief Network for Lung Nodule Segmentation and Cancer Detection. Preprints2022, 2022060217. https://doi.org/10.20944/preprints202206.0217.v1
APA Style
GN, B. B., Sirisha, N., Kezia Rani, B., & Bethu, S. (2022). A Frame Work Design of Chicken-Sine Cosine Algorithm-based Deep Belief Network for Lung Nodule Segmentation and Cancer Detection. Preprints. https://doi.org/10.20944/preprints202206.0217.v1
Chicago/Turabian Style
GN, B. B., B. Kezia Rani and Srikanth Bethu. 2022 "A Frame Work Design of Chicken-Sine Cosine Algorithm-based Deep Belief Network for Lung Nodule Segmentation and Cancer Detection" Preprints. https://doi.org/10.20944/preprints202206.0217.v1
Abstract
Malignant growth is the most widely recognized repulsive infections winning around the world, and the patients with disease are saved just when the malignant growth is distinguished at the beginning phase. Each kind of disease is interesting, with its own arrangement of development properties and hereditary changes. This paper presents the lung knob division and disease characterization by proposing an enhancement calculation. The general technique of the created approach includes four stages, such as pre-processing, division, highlight extraction, and the order. From the outset, the CT picture of the lung is taken care of to the division. When the division is done, the highlights are extricated through morphological and measurable and surface highlights like LOOP and LGP. At long last, the extricated highlights are given to the order step. Here, the characterization is done dependent on the Deep Belief Network (DBN) which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm (CSCA) which distinguish the lung tumor, giving two classes in particular, knob or non-knob. The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specific, precision, affectability, and the explicitness.
Keywords
Chicken-sine Cosine algorithm; Deep Belief Network; Lung nodule detection
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.