RETRACTED: Novel <scp>computer‐aided</scp> lung cancer detection based on <scp>convolutional neural network</scp>‐based and feature‐based classifiers using metaheuristics

Guo, Zhiqiang; Xu, Liyun; Si, Yujuan; Razmjooy, Navid

doi:10.1002/ima.22608

Cited by 62 publications

(30 citation statements)

References 61 publications

(98 reference statements)

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“…The main idea behind this type of structure is to exploit this data series. The name of this neural network is derived from the fact that these types of networks operate recurrently 15 . That is, you perform an operation for each element of a sequence and its output depends on the current input and previous operations.…”

Section: Methodsmentioning

confidence: 99%

“…The name of this neural network is derived from the fact that these types of networks operate recurrently. 15 That is, you perform an operation for each element of a sequence and its output depends on the current input and previous operations. This is done by repeating a network output at time t with the network input at time t + 1 (i.e., the output from the previous step is combined with the new input in the new step).…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

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Compressive strength prediction of structural lightweight concrete by applying the recurrent neural network‐fractional order bat optimization algorithm

Khajehzadeh¹

2022

Concurrency and Computation

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Neural networks methods produce an analysis in various fields. The Neural Network is frequently employed for developing statistical methods for nonlinear systems since, the Neural Networks use the simulation benefits of complicated behavior in several problems. In this study, the Recurrent Neural Network and Recurrent Neural Network based on Fractional Order Bat Optimization Algorithm is utilized for predicting the compressive strength of Lightweight Concrete mixes in 3, 7, 14, and 28 days curing. Recurrent Neural Network has been used for having a comparison. The inputs to the network for the compressive strength are made of eight variables: sand, lightweight fine aggregate, water to cement ratio, silica fume utilized in solution, Lightweight course aggregate, silica fume utilized supplementary to cement, curing period, and superplasticizer. According to the results, the Recurrent Neural Network based on Fractional Order Bat Optimization Algorithm has predicted precise outcomes and trained so fast in comparison with RNN method.by considering the outcomes, the Neural Networks methods are satisfactory mechanisms to evaluate the LWC compressive strength. This can decrease the price and reduce the time of processes in these problems. K E Y W O R D S compressive strength of structural lightweight concrete, fractional order bat optimization algorithm, neural networks methods, recurrent neural network, RNN-FOBOA INTRODUCTIONConcrete is the most practical and widely utilized material in the world. The high-strength Lightweight concrete generation began in the 1970s, and in the 1980s, due to the need of some oil companies in the United States and Norway to build offshore structures and reservoirs such as oil rigs, a wide range of research began to build high-strength Lightweight concrete in these two countries. The results were published in the late 1980s and early 1990s. The largest Lightweight concrete building is a 52-story office building in Texas with a height of 215 m. In recent years, the Lightweight concrete use in the roof slabs of high-rise buildings, bridge decks and other similar items, as well as special applications such as decks and bases of oil rigs have been widely used. 1 The use of Lightweight materials has been considered for a long time and in the modern construction industry, due to the need to build structures with high height and large dimensions, the discussion of lightening has been the focus of more attention. Lightweight concrete with its advantages such as availability in most areas and also being economical can be one of the options in the discussion of designing Lightweight structures. The scope of application of this type of material can also be as a member of instruments

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Section: Methodsmentioning

confidence: 99%

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Compressive strength prediction of structural lightweight concrete by applying the recurrent neural network‐fractional order bat optimization algorithm

Khajehzadeh¹

2022

Concurrency and Computation

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“…The experimentation was conducted on public available LIDC-IDRI dataset. Guo et al [23], developed a sequential method for the detection of lung cancer from CT lung images. The method comprised of two classifiers: CNN-based and feature-based classifier.…”

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

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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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“…Artificial neural networks (ANNs) are computational systems that are inspired by but not necessarily related to biological neural networks. Today, a variety of neural network algorithms are widely used to solve "machine learning" problems [19]. Indeed, the use of neural networks in various issues is of interest to researchers.…”

Section: Optimized Neural Network Based On Bat Optimization Algorithmmentioning

confidence: 99%

Optimized Neural Network for Research Evaluation of Mineral Resources Carrying Capacity in Southern Shaanxi

YiCan

Wei

Arshaghi

2022

Mathematical Problems in Engineering

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Evaluation of the carrying capacity of mineral resources is one of the important research content in the implementation of sustainable development. Based on analyzing the metallogenic geological characteristics, distribution, and resource status of mineral resources in southern Shaanxi, this paper establishes an analysis model of mineral resources and mineral advantages based on the analytic hierarchy process and applies them to evaluate the advantages of mineral resources. To provide optimal and efficient results, an improved model of an artificial neural network based on the bat optimization algorithm has been utilized. Through model analysis, the potential value and carrying capacity of mineral resources in three major prefecture-level cities in southern Shaanxi are comprehensively evaluated and analyzed. The results show that the main dominant minerals in southern Shaanxi are gold, lead zinc, and molybdenum ore. There are three grades of mineral resources carrying capacity: Shangluo City is an excellent grade, Hanzhong City is a good grade, and Ankang City is a general grade.

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RETRACTED: Novel computer‐aided lung cancer detection based on convolutional neural network‐based and feature‐based classifiers using metaheuristics

Cited by 62 publications

References 61 publications

Compressive strength prediction of structural lightweight concrete by applying the recurrent neural network‐fractional order bat optimization algorithm

Compressive strength prediction of structural lightweight concrete by applying the recurrent neural network‐fractional order bat optimization algorithm

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Optimized Neural Network for Research Evaluation of Mineral Resources Carrying Capacity in Southern Shaanxi

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