Lung Cancer Segmentation and Detection using Cuckoo Search Evolutionary Algorithms in Early Stage

Thakur, Gaurav; Wasson, Vikas

doi:10.1109/smartgencon51891.2021.9645876

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The firefly technique fuzzy C-means (FA-FCM) identification procedure was used to carry over the nodule segmentation process. In 2021, Thakur et al used the hybrid K-means for the segmentation of the MNIST dataset [27]. The authors used the feature extraction approach to extract the lung region, and the CNN technique was also used for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

Shamas,

Panda,

Sharma

et al. 2024

CMES

View full text Add to dashboard Cite

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarm intelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC), K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeans with Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer Action Program (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics: precision, sensitivity, specificity, f-measure, accuracy, Matthews Correlation Coefficient (MCC), Jaccard, and Dice. The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved the quality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancer images. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasure of 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove that K-means with ABC, K-means with PSO, K-means with FFA, and K-means with CSA have achieved an improvement of 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentation for lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significant improvement in accuracy, hence can be utilized by researchers for improved segmentation processes of medical image datasets for identifying the targeted region of interest.

show abstract

Section: Literature Reviewmentioning

confidence: 99%