An Automated Computer System Based on Genetic Algorithm and Fuzzy Systems for Lung Cancer Diagnosis

Alharbi, Abir

doi:10.1515/ijnsns-2017-0048

Cited by 11 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The imaging diagnosis combined with tumor markers had the highest sensitivity of different types of lung cancer, and the sensitivity to lung cancer (90%) was superior to other diagnostic methods (P < 0.05), as shown in Tables 3, 4 and Figure 3.…”

Section: Comparison Of Sensitivity and Specificity In The Combined Diagnosismentioning

confidence: 92%

See 1 more Smart Citation

Diagnostic Value of Imaging Combined With Tumor Markers in Early Detection of Lung Cancer

Wei

Wang

et al. 2021

Front. Surg.

View full text Add to dashboard Cite

Objective: The objective of this research is to explore the diagnostic value of imaging plus tumor markers in the early detection of lung cancer.Methods: Sixty patients with lung cancer treated in our hospital from January 2018 to January 2019 were selected as group A. They were matched with 60 patients with benign lung disease as group B and 60 healthy subjects examined in our hospital as group C. The carcino-embryonic antigen (CEA), CYFRA21-1, and neuron-specific enolase (NSE) were assessed, and the diagnostic value of tumor markers plus imaging in lung cancer diagnosis was explored.Results: The CEA, CYFRA21-1, and NSE in group A were evidently superior to those in groups B and C, and those in group B were superior to those in group C (all P < 0.001). CEA had the highest sensitivity (56.7%), and NSE had the highest specificity (93.3%). The tumor markers plus imaging had the highest sensitivity for different types of lung cancer, and the sensitivity to early lung cancer (90%) was superior to other diagnostic methods (P < 0.05).Conclusion: The tumor markers plus imaging is of great significance in early lung cancer diagnosis and provides a reference for judging the pathological classification.

show abstract

Section: Comparison Of Sensitivity and Specificity In The Combined Diagnosismentioning

confidence: 92%

“…With the development of multi-slice spiral CT technology, it has been widely used in practice. With the merit of being clear, simple, and efficient, it has played a certain positive role in early lung cancer diagnosis (1)(2)(3)(4). However, studies that are concerned about the combination with CT and tumor markers are scant.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Value of Imaging Combined With Tumor Markers in Early Detection of Lung Cancer

Wei

Wang

et al. 2021

Front. Surg.

View full text Add to dashboard Cite

show abstract

“…The results show that C4.5 performs better in predicting lung cancer as the training data set is increased. In [8] a combined genetic fuzzy algorithm is proposed to detect lung cancer. He used that algorithm on 32 patients with 56 attributes and achieved 97.5 percent accuracy with a confidence level of 93 percent.…”

Section: Literature Reviewmentioning

confidence: 99%

Diagnosis of breast cancer using machine learning algorithms based on features selected by Genetic Algorithm: Assessed on five datasets

Saranya¹,

S²

2021

JUSST

View full text Add to dashboard Cite

Breast Cancer is one of the chronic diseases occurred to human beings throughout the world. Early detection of this disease is the most promising way to improve patients’ chances of survival. The strategy employed in this paper is to select the best features from various breast cancer datasets using a genetic algorithm and machine learning algorithm is applied to predict the outcomes. Two machine learning algorithms such as Support Vector Machines and Decision Tree are used along with Genetic Algorithm. The proposed work is experimented on five datasets such as Wisconsin Breast Cancer-Diagnosis Dataset, Wisconsin Breast Cancer-Original Dataset, Wisconsin Breast Cancer-Prognosis Dataset, ISPY1 Clinical trial Dataset, and Breast Cancer Dataset. The results exploit that SVM-GA achieves higher accuracy of 98.16% than DT-GA of 97.44%.

show abstract

“…One of these feature selection methods that processes the features according to their rank without overlooking the correlation between them is a heuristic search method and one of these heuristic approaches is the GA. [38] The genetic algorithm method is an iterative procedure that involves a population representing the search space for possible solutions to the given optimization problem. [57] An individual is a possible solution amongst these possible solutions in the population. Every individual has a chromosome, which is a bit encoding of the representative solution, generally, expressed in binary format.…”

Section: Genetic Algorithm For Feature Se-lectionmentioning

confidence: 99%

“…Every individual has a chromosome, which is a bit encoding of the representative solution, generally, expressed in binary format. [57] Fitness is a characteristic measurement of how fit or good the individual is and is a sign whether the individual will be selected for the reproduction stage or not. The basic genetic algorithm proceeds as follows: an initial population of members is generated heuristically or at random.…”

Section: Genetic Algorithm For Feature Se-lectionmentioning

confidence: 99%

A Genetic-LVQ neural networks approach for handwritten Arabic character recognition

Alharbi¹

2018

AIR

Self Cite

View full text Add to dashboard Cite

Handwritten recognition systems are a dynamic field of research in areas of artificial intelligence. Many smart devices available in the market such as pen-based computers, tablets, mobiles with handwritten recognition technology need to rely on efficient handwritten recognition systems. In this paper we present a novel Arabic character handwritten recognition system based on a hybrid method consisting of a genetic algorithm and a Learning vector quantization (LVQ) neural network. Sixty different handwritten Arabic character datasets are used for training the neural network. Each character dataset contains 28 letters written twice with 15 distinct shaped alphabets, and each handwritten Arabic letter is represented by a binary matrix that is used as an input to a genetic algorithm for feature selection and dimension reduction to include only the most effective features to be fed to the LVQ classifier. The recognition process in the system involves several essential steps such as: handwritten letter acquisition, dataset preparation, feature selection, training, and recognition. Comparing our results to those acquired by the whole feature dataset without selection, and to the results using other classification algorithms confirms the effectiveness of our proposed handwritten recognition system with an accuracy of 95.4%, hence, showing a promising potential for improving future handwritten Arabic recognition devices in the market.

show abstract

An Automated Computer System Based on Genetic Algorithm and Fuzzy Systems for Lung Cancer Diagnosis

Cited by 11 publications

References 37 publications

Diagnostic Value of Imaging Combined With Tumor Markers in Early Detection of Lung Cancer

Diagnostic Value of Imaging Combined With Tumor Markers in Early Detection of Lung Cancer

Diagnosis of breast cancer using machine learning algorithms based on features selected by Genetic Algorithm: Assessed on five datasets

A Genetic-LVQ neural networks approach for handwritten Arabic character recognition

Contact Info

Product

Resources

About