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

Computer Modeling in Engineering & Sciences 2024年第2期138卷 1051-1075页

作者： Shazia Shamas surya narayan panda Ishu Sharma Kalpna Guleria Aman Singh Ahmad Ali AlZubi Mallak Ahmad AlZubi Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Higher Polytechnic School Universidad Europea del AtlánticoC/Isabel Torres 21Santander39011Spain Department of Engineering Universidad Internacional IberoamericanaAreciboPR00613USA Uttaranchal Institute of Technology Uttaranchal UniversityDehradunUttarakhand248007India Computer Science Department King Saud UniversityRiyadhSaudi Arabia Faculty of Medicine Jordan University of Science and TechnologyIrbidJordan

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent 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 Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (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 *** detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by resea

关键词： Lesion lung cancer segmentation medical imaging meta-heuristic Artificial Bee Colony(ABC) Cuckoo Search Algorithm(CSA) Particle Swarm Optimization(PSO) Firefly Algorithm(FFA) segmentation

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Themo-Physical Properties of Al-Mg Alloy in Liquid State at Different Temperatures

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Materials Sciences and Applications 2018年第10期9卷 812-828页

作者： Shashit Kumar Yadav Lok narayan Jha Arjun Dhungana Upendra Mehta Devendra Adhikari Department of Physics Surya Narayan Satya Narayan Morbaita Yadav Multiple Campus Tribhuvan University Siraha Nepal Central Department of Physics Tribhuvan University Kirtipur Nepal Department of Physics Mahendra Morang Adarsha Multiple Campus Tribhuvan University Biratnagar Nepal

Thermo-physical properties of Al-Mg alloys in molten state at 1073 K have been studied using thermodynamic modeling. Thermodynamic properties, such as free energy of mixing, heat of mixing, entropy of mixing, activities and structural properties, such as concentration fluctuation in long wavelength limit, Warren-Cowely short range order parameter have been studied at 1073 K, 1173 K, 1273 K and 1373 K on the basis of regular associated solution model. The surface properties such as surface concentrations and surface tension of the liquid alloys have been studied by using Butler’s model. A consistent set of model parameters have also been obtained by using optimization procedure based on statistical thermodynamics. Our analysis reveals that Al-Mg alloy is moderately interacting and it shows ordering nature at 1073 K. The nature of the alloys changes from ordering to segregating as the temperature increases.

关键词： True Mole Fraction Pairwise Interaction Energy Temperature Derivative Terms Al3Mg2 Complex Optimization Procedure

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