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Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution Model

Debnath Bhattacharyya1, Eali. Stephen Neal Joshua2, N. Thirupathi Rao2, Yung-cheol Byun3,*

1 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522302, Andhra Pradesh, India
2 Department of Computer Science & Engineering, Vignan’s Institute of Information Technology (A), Visakhapatnam, 530049, Andhra Pradesh, India
3 Department of Computer Engineering, Jeju National University, 102 Jejudaehak-ro, Jeju-si, Jeju-do, 690-756, Korea

* Corresponding Author: Yung-cheol Byun. Email: email

Computers, Materials & Continua 2023, 75(1), 1447-1465.


Lung cancer is the leading cause of mortality in the world affecting both men and women equally. When a radiologist just focuses on the patient’s body, it increases the amount of strain on the radiologist and the likelihood of missing pathological information such as abnormalities are increased. One of the primary objectives of this research work is to develop computer-assisted diagnosis and detection of lung cancer. It also intends to make it easier for radiologists to identify and diagnose lung cancer accurately. The proposed strategy which was based on a unique image feature, took into consideration the spatial interaction of voxels that were next to one another. Using the U-NET+Three parameter logistic distribution-based technique, we were able to replicate the situation. The proposed technique had an average Dice co-efficient (DSC) of 97.3%, a sensitivity of 96.5% and a specificity of 94.1% when tested on the Luna-16 dataset. This research investigates how diverse lung segmentation, juxta pleural nodule inclusion, and pulmonary nodule segmentation approaches may be applied to create Computer Aided Diagnosis (CAD) systems. When we compared our approach to four other lung segmentation methods, we discovered that ours was the most successful. We employed 40 patients from Luna-16 datasets to evaluate this. In terms of DSC performance, the findings demonstrate that the suggested technique outperforms the other strategies by a significant margin.


Cite This Article

APA Style
Bhattacharyya, D., Joshua, E.S.N., Rao, N.T., Byun, Y. (2023). Lung cancer segmentation with three-parameter logistic type distribution model. Computers, Materials & Continua, 75(1), 1447-1465.
Vancouver Style
Bhattacharyya D, Joshua ESN, Rao NT, Byun Y. Lung cancer segmentation with three-parameter logistic type distribution model. Comput Mater Contin. 2023;75(1):1447-1465
IEEE Style
D. Bhattacharyya, E.S.N. Joshua, N.T. Rao, and Y. Byun "Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution Model," Comput. Mater. Contin., vol. 75, no. 1, pp. 1447-1465. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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