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

Vijh, Surbhi; Gaurav, Prashant; Pandey, Hari Mohan

doi:10.1007/s00521-020-05362-z

Cited by 39 publications

(25 citation statements)

References 59 publications

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“…Moreover, SqueezeNet and ResNet are executed together for the classification of lung nodule. Vijh et al [20] introduced the hybrid bio-inspired Whale optimization algorithm with adaptive particle swarm optimization (WOA-APSO) for feature selection with convolutional neural network for classification of lung cancer nodules. The Wiener filter has been applied to eliminate noise from CT image in preprocessing.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Naseer¹,

Masood²,

Akram³

et al. 2023

Computers, Materials &Amp; Continua

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Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine (SVM) algorithm namely LungNet-SVM. The proposed model consists of seven convolutional layers, three pooling layers, and two fully connected layers used to extract features. Support vector machine classifier is applied for the binary classification of nodules into benign and malignant. The experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016 (LUNA16). The proposed model has achieved 97.64% of accuracy, 96.37% of sensitivity, and 99.08% of specificity. A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung cancer. The experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

“…[17,18,28] requires to improve the detection accuracy. The study [21,23] was based on an imbalanced dataset while other research works [19,20,26,30,31] are based on a complex model.…”

Section: Related Workmentioning

confidence: 99%

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Naseer¹,

Masood²,

Akram³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Few popular optimization algorithms are artificial bee colony (ABC) [27] [71] [72] [73], particle swarm optimization (PSO) [28], whale optimization algorithm (WOA) [29], genetic algorithm (GA) [30], adaptive particle swarm optimization [31], cuckoo search algorithm [32], grey wolf optimization [33], cat swarm optimization [34], and lion optimization algorithm [35]. These optimization algorithms provide the optimal global solution for the selected set of features through exploitation and exploration [36,37].…”

Section: Related Workmentioning

confidence: 99%

Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis

Vijh¹,

Pandey

Gaurav³

2022

Neural Comput & Applic

Self Cite

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This paper presents an efficient skull stripping method to improve the decision-making process. Extended weiner filtering (EWF) is used for removing the noise and enhancing the quality of images. Further, laplacian lion optimization algorithm (LXLOA) is implemented. LXLOA utilizes the Otsu's and Tsallis entropy fitness function to determine an optimal solution. The implemented LXLOA provides a threshold value required for performing the segmentation on the brain MRI images. The extracted features are selected using fuzzy weighted k-means embedding LDA (linear discriminant analysis) method for improving training of the classification model. The proposed LXLOA is extensively tested on standard benchmark functions CEC 2017 and outperforms the existing state-of-the-art algorithm. Rigorous statistical analysis is conducted to determine the statistical significance. Three-fold performance comparison is performed by considering (a) the quality of the segmented image; (b) accuracy, sensitivity, and specificity; and (c) computational cost of convergence for finding an optimal solution. Result reveals that LXLOA gives promising results and demonstrate effective outcomes on the standard quality measures (a) accuracy (97.37%); (b) sensitivity (85.8%); (c) specificity (90%); and (d) precision (91.92%).

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“…Vijh et al [ 89 ] suggested a hybrid bionic approach that combines the benefits of whale optimization and adaptive particle swarm optimization. In this method, 120 lung CT images were preprocessed and segmented using CNN to obtain segmented nodules in tumor and non-tumor areas.…”

Section: Practical Applications Of Convolutional Neural Network In Tumor Diagnosismentioning

confidence: 99%

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

Yan

Yao

Wang

et al. 2021

Biology

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Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient’s secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future.

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

Cited by 39 publications

References 59 publications

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

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