Lung cancer segmentation from CT scan images using modified mayfly optimization and particle swarm optimization algorithm

The development of a computer-aided detection system is a critical component of clinical decision-making As the death rate grows, cancer has become a major concern for both men and women. The radiologists need to accurately pinpoint the region of the lung tumor to offer proper radiation therapy for...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Multimedia tools and applications Jg. 83; H. 2; S. 3567 - 3584
Hauptverfasser: Poonkodi, S., Kanchana, M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.01.2024
Springer Nature B.V
Schlagworte:
Algorithms
Artificial neural networks
Computed tomography
Computer Communication Networks
Computer Science
Critical components
Data Structures and Information Theory
Image contrast
Image enhancement
Image quality
Image segmentation
Lung cancer
Lung diseases
Medical imaging
Model accuracy
Multimedia Information Systems
Optimization
Particle swarm optimization
Radiation therapy
Special Purpose and Application-Based Systems
Tumors
Automatic segmentation
Ensemble Deep Convolutional Neural Network
Lung computed tomography
Lung cancer
Modified mayfly optimization
ISSN:1380-7501, 1573-7721
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The development of a computer-aided detection system is a critical component of clinical decision-making As the death rate grows, cancer has become a major concern for both men and women. The radiologists need to accurately pinpoint the region of the lung tumor to offer proper radiation therapy for lung cancer patients. Due to low-image quality, higher computational difficulties, and other reasons, the existing lung cancer segmentation methods failed to provide better segmentation accuracy. To overcome these challenges, we proposed a novel approach for lung tumor segmentation. Initially, the input CT scan image contrast level is increased using histogram equalization (HE) during pre-processing. The adaptive bilateral filter (ABF) provides enhanced CT scan images for de-noising. Next to pre-processing, we introduced an ensemble deep convolutional neural network (EDNN) based on Modified mayfly optimization and modified particle swarm optimization (M 2 PSO) algorithm for the segmentation of lung cancer from the pre-processed CT images. The proposed model accurately segments the lung disease tumor without manual supervision and the need for fully annotated data. Finally, the measures like dice similarity score (DSS), precision, sensitivity, dice loss, and generalized dice loss analyze the performance of the proposed model. Based on the experimental investigations, the proposed EDCNN- M 2 PSO algorithm demonstrated superior performance in terms of lung tumor segmentation than other existing techniques. The proposed model has average accuracy, sensitivity, and precision scores of 97%, 98%, and 98%, respectively. The proposed model's DSS value is 98.6%, which is relatively higher than the existing approaches.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-023-15688-0