MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier

Hassanien, Aboul Ella; Moftah, Hossam M.; Azar, Ahmad Taher; Shoman, Mahmoud

doi:10.1016/j.asoc.2013.08.011

Cited by 86 publications

(15 citation statements)

References 17 publications

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“…Curvelet frequency is used to improve the ef iciency (Francis et al, 2014). MRI breast cancer diagnosis hybrid approach using adaptive antbased segmentation and multilayer perceptron neural networks classi ier hybrid approach that combines the advantages of fuzzy sets, ant-based clustering and multilayer perceptron neural networks (MLPNN) classi ier, in conjunction with statisticalbased feature extraction technique (Hassanien et al, 2014).…”

Section: Figure 2: Automated Diagnosis System For Detection Of Breastmentioning

confidence: 99%

Exploration and evaluation of efficient pre-processing and segmentation technique for breast cancer diagnosis based on mammograms

N¹,

Rao²

2019

ijrps

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Breast cancer is the second leading cause of death for women everywhere in the world. Since the reason behind the disease remains unknown, early detection and diagnosis is the key challenge for breast cancer control. In this work, mammogram images are initially subject to pre-processing using Laplacian ilter for enhancement of tumour regions, Gaussian mixture model, Gaussian kernel FCM, Otsu global thresholding and FCM technique are employed for segmentation. Further, the ef iciency of segmentation techniques is analyzed by classifying the samples into benign, malignant and healthy using Gray Level Co-occurrence Matrix (GLCM) features. Linear discriminant analysis classiier is used a combination based on which ef iciency used for classi ication of mammograms. Ensemble methods are evaluated. The ef iciency has resulted in better accuracy with the ensemble-based method. The experimentation is conducted in the mini MIAS database of mammograms, and the ef iciency of the linear discriminant analyzer is found to be 89.19% for GKFCM, 83.78% with Otsu and 78.38% with FCM method with GLCM features.

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Section: Figure 2: Automated Diagnosis System For Detection Of Breastmentioning

confidence: 99%

Exploration and evaluation of efficient pre-processing and segmentation technique for breast cancer diagnosis based on mammograms

N¹,

Rao²

2019

ijrps

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“…Hassanien et al [12] introduces a hybrid approach with a combination of the benefits of fuzzy sets, ant-based clustering and multilayer perception neural networks (MLPNN) classifier, in conjunction with statisticalbased feature extraction technique. They developed a system with an algorithm that was based on type-II fuzzy sets for enhancing the contrast of the input images.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of a Fuzzy Expert System for Diagnosing Breast Cancer

Okikiola

Mustapha

et al. 2019

JAMCS

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The death rate is caused by breast cancer in women is increasingly high and growing. A number of people are getting to lose this part of their body due to late diagnosis of this disease. This therefore requires the development of an efficient and accurate diagnosis approach that will aid providing the knowledge of the type of breast cancer type and severity in order to reduce the mortality rate through the disease. This need serves as the major motivation for this work. In this paper, we proposed a fuzzy expert system for diagnosis of and treatment recommendation of breast cancer problems which provide physicians and patients with information of the cancer type and treatment recommendation. The application was designed using JAVA programming language, MATLAB and SQLite database engine. This application permits update of new information as a means of knowledge. The evaluation showed that the inclusion of the fuzzy inference system improved the accuracy and precision of the system from 0.8 to 0.9. The system is user-friendly and has high level of acceptability from the validation conducted at the end of the research.

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“…In Table 2(Tab. 2) (References in Table 2: Al-Faris et al, 2014[6]; Kim et al, 2014[75]; Dheeba et al, 2014[34]; Al-Faris et al, 2014[7]; Hassanien et al, 2014[57]; Kannan et al, 2011[73]) a few examples of segmentation methods in breast CAD systems collected are shown. …”

Section: Cornerstones Of a Cad Systemmentioning

confidence: 99%

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Jalalian

Mashohor

Mahmud

et al. 2017

EXCLI Journal; 16:Doc113; ISSN 1611-2156

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MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier

Cited by 86 publications

References 17 publications

Exploration and evaluation of efficient pre-processing and segmentation technique for breast cancer diagnosis based on mammograms

Exploration and evaluation of efficient pre-processing and segmentation technique for breast cancer diagnosis based on mammograms

Design and Implementation of a Fuzzy Expert System for Diagnosing Breast Cancer

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

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