An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier

Manoharan, Prabukumar; Agilandeeswari, L.; Ganesan, K.

doi:10.1007/s12652-017-0655-5

Cited by 83 publications

(41 citation statements)

References 57 publications

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“…But, the specificity was not improved in early discussed paper (Amiri and Mahmoudi, 2016). The earlier cancer detection of cuckoo search optimization and support vector machine in (Prabukumar, et al, 2017), the data segmented by Fuzzy C mean, then features selected by Cuckoo search. Not only used for earlier detection, mainly applied for the efficient accuracy of cancer detection.…”

Section: Resultsmentioning

confidence: 99%

Distributed ICSA Clustering Approach for Large Scale Protein Sequences and Cancer Diagnosis

Thenmozhi¹,

N²,

Shanthi³

et al. 2018

Asian Pac J Cancer Prev

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Objective:With the over saturating growth of biological sequence databases, handling of these amounts of data has increasingly become a problem. Clustering has become one of the principal research objectives in structural and functional genomics. However, exact clustering algorithms, such as partitioned and hierarchical clustering, scale relatively poorly in terms of run time and memory usage with large sets of sequences.Methods:From these performance limits, heuristic optimizations such as Cuckoo Search Algorithm with genetic operators (ICSA) algorithm have been implemented in distributed computing environment. The proposed ICSA, a global optimized algorithm that can cluster large numbers of protein sequences by running on distributed computing hardware.Results:It allocates both memory and computing resources efficiently. Compare with the latest research results, our method requires only 15% of the execution time and obtains even higher quality information of protein sequence.Conclusion:From the experimental analysis, We noticed that the cluster of large protein sequence data sets using ICSA technique instead of only alignment methods reduce extremely the execution time and improve the efficiency of this important task in molecular biology. Moreover, the new era of proteomics is providing us with extensive knowledge of mutations and other alterations in cancer study.

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Section: Resultsmentioning

confidence: 99%

Distributed ICSA Clustering Approach for Large Scale Protein Sequences and Cancer Diagnosis

Thenmozhi¹,

N²,

Shanthi³

et al. 2018

Asian Pac J Cancer Prev

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“…The structural and texture extracted features are used in the classification performed by the support vector machine. Prabukumar et al [45] proposed a hybrid segmentation technique comprising Fuzzy C-means (FCM) and region growing algorithm to segment the nodule. The statistical, texture and geometrical features are extracted from the segmented nodule, and the optimized features are selected using a cuckoo search algorithm.…”

Section: Related Workmentioning

confidence: 99%

Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

Vijh¹,

Gaurav

Pandey

2020

Neural Comput & Applic

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In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction.

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“…A number of researchers have focused on optimizing the parameters of kernel functions to obtain better classification accuracy in cancer diagnosis. M. Prabukumar et al [30] used SVM classifier to identify the lung cancer and more than 98% accuracy was achieved. The grid search method was employed to search for the optimal parameters in this study.…”

Section: Related Workmentioning

confidence: 99%

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

2019

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As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.

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An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier

Cited by 83 publications

References 57 publications

Distributed ICSA Clustering Approach for Large Scale Protein Sequences and Cancer Diagnosis

Distributed ICSA Clustering Approach for Large Scale Protein Sequences and Cancer Diagnosis

Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

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