Detection of Lesion in Mammogram Images Using Differential Evolution Based Automatic Fuzzy Clustering

Srikrishna, A.; Reddy, B. Eswara; Srinivas, V. Sesha

doi:10.1007/978-981-10-0308-0_5

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…In four levels of analysis, features are selected, and the best average classification rate in the fourth level of analysis by selecting 16 features out of 72 features is 89.47% [10]. The system presented by Srikrishna et al (2016) uses an accurate identification using differential evolution based on a fuzzy clustering algorithm to detect lesions in mammography images. The location of the lesion that the radiologist marked manually was compared with the lesion obtained automatically by the system.…”

Section: Magenda Et Al (2015) Have Investigated the Classification Pe...mentioning

confidence: 99%

“…The location of the lesion that the radiologist marked manually was compared with the lesion obtained automatically by the system. Segmentation with a fuzzy clustering-based differential evolution algorithm is close to the radiologist's actual marking and can be proposed for medical diagnosis [11]. The research by Zhang et al (2016) has developed a new method based on wavelet entropy energy and linear regression classification.…”

Section: Magenda Et Al (2015) Have Investigated the Classification Pe...mentioning

confidence: 99%

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Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast's Tumor

Rituraj¹

2023

Preprint

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Breast cancer is the second leading cause of death after lung cancer. The only possible way to save patients' lives is early diagnosis of the disease; Because if this disease is diagnosed in the early stages and with a high level of accuracy, the chance of survival increases. Different fuzzy-based soft computing techniques have been proposed. In this research, the proposed fuzzy hybrid algorithm - particle swarm has been used to detect the type of breast tumors based on the analysis of features in mammography images. The proposed method in this study, the fuzzy hybrid algorithm - the proposed particle swarm algorithm, has a remarkable performance of 94.58% in breast cancer diagnosis. The results obtained from this study can be used for timely diagnosis and providing effective treatments for breast cancer

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Section: Magenda Et Al (2015) Have Investigated the Classification Pe...mentioning

confidence: 99%

Section: Magenda Et Al (2015) Have Investigated the Classification Pe...mentioning

confidence: 99%

Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast's Tumor

Rituraj¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In fuzzy logic, the problem is modeled as a group of "IF-THEN" rules, unlike the classical methods which model the problem by expressing complex mathematical relations. Fuzzy logic was utilized in various applications [1][2][3][4][5]]. When the efficiency of type-1 fuzzy (T1F) sets are approved in many research papers, Zadeh et al [3] presented the second series of fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

Memristor Crossbar-Based Hardware Implementation of Type-2 Fuzzy Membership Function and On-Chip Learning

Klidbary

Javadian

Omidi

et al. 2021

IJE

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Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast’s Tumor

Ghorbani

Lajmorak

Ghorbani

et al. 2023

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI)

View full text Add to dashboard Cite

Breast cancer is the second leading cause of death after lung cancer. The only possible way to save patients' lives is early diagnosis of the disease; Because if this disease is diagnosed in the early stages and with a high level of accuracy, the chance of survival increases. Different fuzzy-based soft computing techniques have been proposed. In this research, the proposed fuzzy hybrid algorithm -particle swarm has been used to detect the type of breast tumors based on the analysis of features in mammography images. The proposed method in this study, the fuzzy hybrid algorithm -the proposed particle swarm algorithm, has a remarkable performance of 94.58% in breast cancer diagnosis. The results obtained from this study can be used for timely diagnosis and providing effective treatments for breast cancer.

show abstract

Detection of Lesion in Mammogram Images Using Differential Evolution Based Automatic Fuzzy Clustering

Cited by 4 publications

References 2 publications

Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast's Tumor

Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast's Tumor

Memristor Crossbar-Based Hardware Implementation of Type-2 Fuzzy Membership Function and On-Chip Learning

Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast’s Tumor

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