Automated grading of non-small cell lung cancer by fuzzy rough nearest neighbour method

Moitra, Dipanjan; Mandal, Rakesh Kr.

doi:10.1007/s13721-019-0204-6

Cited by 17 publications

(4 citation statements)

References 38 publications

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“…Recently, several methods of soft computing have been adopted to develop efficiency in computer-aided diagnosis (CAD)-based lung cancer diagnosis systems. Examples include fuzzy systems, 4 optimization algorithms, 5 neural networks, 6 and deep-learning methods. 7 Previously, the diagnosis of the tumor was based on machine learning and various statistical features, including geometry, moment, color, and texture features.…”

Section: Introductionmentioning

confidence: 99%

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

Guo

et al. 2021

Int J Imaging Syst Tech

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This study proposes a lung cancer diagnosis system based on computed tomography (CT) scan images for the detection of the disease. The proposed method uses a sequential approach to achieve this goal. Consequently, two well‐organized classifiers, the convolutional neural network (CNN) and feature‐based methodology, have been used. In the first step, the CNN classifier is optimized using a newly designed optimization method called the improved Harris hawk optimizer. This method is applied to the dataset, and the classification is commenced. If the disease cannot be detected via this method, the results are conveyed to the second classifier, that is, the feature‐based method. This classifier, including Haralick and LBP features, is subsequently applied to the received dataset from the CNN classifier. Finally, if the feature‐based method also does not detect cancer, the case study is healthy; otherwise, the case study is cancerous.

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Section: Introductionmentioning

confidence: 99%

“…Recently, several methods of soft computing have been adopted to develop efficiency in computer‐aided diagnosis (CAD)‐based lung cancer diagnosis systems. Examples include fuzzy systems, 4 optimization algorithms, 5 neural networks, 6 and deep‐learning methods 7 …”

Section: Introductionmentioning

confidence: 99%

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

Guo

et al. 2021

Int J Imaging Syst Tech

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show abstract

“…However, it is possible that there are other ML methods that may perform better. For example, k-means [50], nearest neighbour [51], linear discriminant analysis [52], hidden Markov [53] are other ML methods that can be used to compare prediction accuracy. By employing these additional methods, it may be possible to identify the most accurate method.…”

Section: Future Improvement On ML Modelsmentioning

confidence: 99%

A Feasibility Study of Machine Learning Models for Cancer Rate Prediction

Chen¹,

Ding²

2023

Preprint

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Cancer is a major concern for people, and accurately predicting the probability of cancer incidence and mortality is an important research topic. With the development of big data and artificial intelligence technology, a new machine learning model has emerged. Using 72,591 pieces of data, including age, case count, population size, race, gender, site of onset, and year of discovery, we built a machine learning model. Through calculations, we found that the decision tree, random forest, logistic regression, support vector machine, and neural network achieved testing accuracies of 62.11%, 61.68%, 54.53%, 55.72%, and 63.10%, respectively. With the help of this model, scientists and policymakers can accurately predict future cancer incidence and mortality rates through databases, allowing them to make relevant policies (such as timely allocating doctors and medical resources) to better serve the people.

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“…Approximately 25% of cancer deaths are related to LC, and morbidity and mortality have shown a significant upward trend [4,5]. As a result, the accurate screening and early diagnosis of LC would be extremely appealing to increase the chance of successful treatment and survival [6][7][8]. Computed tomography (CT) image findings, such as nodule size, density, and growth, are mostly used to evaluate LC [2].…”

Section: Introductionmentioning

confidence: 99%

A habituation memristor model for lung cancer screening application

Li,

Feng,

Fang

et al. 2023

Phys. Scr.

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Lung cancer screening is critical to the diagnosis and treatment of patients. Today, computed tomography (CT) scanning technology provides a promising approach for the screening of lung cancer. Nevertheless, the redundant information in CT images often limits the efficiency and accuracy of screening. The biological sensory nervous system has an important mechanism for screening out redundant information, namely habituation. Here, we designed a second-order memristor model with habituation characteristics. Some of the habituation behavior of the memristor model has been demonstrated with LTspice simulation. Furthermore, the habituation memristor model is incorporated in a volatile memristor based leaky integrate and fire (LIF) neuron circuit to construct a simple neural system. The simulation results indicate that the neural system exhibits reliable habituation behaviors. Finally, lung cancer screening tasks have been implemented based on the neural system with habituation behavior. The habituation memristor circuit serves as a data preprocessing layer that filters out relevant information from lung cancer images. The results indicate that the performance and accuracy of lung cancer screening performance are noticeably better than the neural system without habituation behavior. This work provides a new idea for lung cancer screening implementation.

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Automated grading of non-small cell lung cancer by fuzzy rough nearest neighbour method

Cited by 17 publications

References 38 publications

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

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

A Feasibility Study of Machine Learning Models for Cancer Rate Prediction

A habituation memristor model for lung cancer screening application

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