Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses

Ashraf, Mohammad; Le, Kim; Huang, Xu

doi:10.1109/iccit.2010.5711189

Cited by 16 publications

(5 citation statements)

References 10 publications

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“…In an evaluation on a BC dataset, they found that their technique was 97.38% accurate using 10-fold cross-validation. The adaptive neuro-fuzzy inference system (ANFIS) and information gain (IG) were used as a feature selection strategy by Ashraf et al [21] to develop a BC diagnosis model. Here, IG is applied to reduce the number of features to the optimal number, and then the dataset is passed to the ANFIS classifier.…”

Section: Related Workmentioning

confidence: 99%

Tumor detection and classification in breast mammography based on fine-tuned convolutional neural networks

Ahmed

Keshk

Abo-Seida

et al. 2021

IJCI. International Journal of Computers and Information

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Breast cancer (BC) is one of the most dangerous diseases for women. Breast screening is a technique performed to discover BC at an early stage and reduce the mortality rate. Mammography, which allows patients to identify changes in their breasts before they feel them, is the primary screening tool for BC diagnosis. In this study, pretrained convolutional neural networks (CNNs) like visual geometry group (VGG) VGG-16 and VGG-19 are implemented to detect and classify breast tumors on the INbreast dataset. In the proposed model, breast images are initially preprocessed to improve image quality and reduce computation time. Then, the parameters learned in the networks are transferred to learn with the breast parameters to improve the classification results. Therefore, this work utilizedto make an efficient manipulation for the obtained information from the large volume of data generated so that that correct classification may enhance the treatment options. Furthermore, in the evaluation stage, four metrics accuracy, sensitivity, specificity, and area under the ROC curve (AUC) were considered to measure the performance of the proposed model. It was found that the proposed model obtained accuracy, sensitivity, specificity, and AUC values of 97.1%, 96.3%, 97.9%, and 0.988%, respectively.

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Section: Related Workmentioning

confidence: 99%

Tumor detection and classification in breast mammography based on fine-tuned convolutional neural networks

Ahmed

Keshk

Abo-Seida

et al. 2021

IJCI. International Journal of Computers and Information

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“…Technique Classification rate (%) [3] IGANFIS 98.24 [32] SANFIS 96.07 [10] L.V .Q 95,82 [15] Fuzzy 96,71 [21] Fuzzy-GA1 97,36 We can say that neuro-fuzzy systems are connectionist models that allow learning as artificial neural network , but their structure can be interpreted as a set of fuzzy rules .Fuzzy logic and neural networks form the basis of the majority aided diagnostic intelligent systems.It would be interesting to combine the two approaches to exploit both advantages.…”

Section: Referencesmentioning

confidence: 99%

“…Our goal is to obtain a high performance with a reasonable number of rules. In this study, we have used information gain algorithm [3] in order to reduce the feature number of the Wisconsin breast cancer database (WBCD), So we obtain 6 features instead of 9. Table 6 shows some results obtained for our experimentation: Initial parameters of membership functions are shown in the following figure 3:…”

Section: Choice Of Parametersmentioning

confidence: 99%

A Neuro-Fuzzy Inference Model for Breast Cancer Recognition

Bekaddour¹

2012

IJCSIT

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Breast cancer is known as one of the most common cancers to afflict the female population. Computer assisted diagnosis can be helpful for doctors in detection and diagnosing of potential abnormalities. Several techniques can be useful for accomplishing this task. This paper outlines an approach for recognizing breast cancer diagnosis using neuro-fuzzy inference technique namely ANFIS (Adaptative Neuro-Fuzzy Inference System). Wisconsin breast cancer diagnosis (WBCD)database developed at University of California, Irvine (UCI) is used to evaluate this method. Results show that the best performances are obtained by our model compared to others cited in literatur (an accuracy of 98, 25 % ).

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“…The lung cores (valleys) including pixels with gray levels less than GL [ 1 ] then should be in Region [ 0 ] (Fig. 3b).…”

Section: Lung Boundary Detection Algorithmmentioning

confidence: 99%

“…The experimental result obtained with an algorithm to detect early nodules for lung cancer and TP is very encouraging. Data mining and other artificial intelligent techniques may be used to make the system becoming an active and powerful expert system [1].…”

Section: E Other Cxr Analysismentioning

confidence: 99%

Chest X-ray analysis for an active distributed E-health system with computer-aided diagnosis

2010

5th International Conference on Computer Sciences and Convergence Information Technology

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The quality of life of a country's citizens is much depended on its healthcare system. People have the right to know the status of their health. Healthcare providers need to know the medical histories of patients to offer better treatment. Therefore, demand for improvement in the access of healthcare information has been increased. This paper presents the design of an active distributed E-health system, which is scalable, and more advanced softwares can be easily added. Some works on chest Xray analysis are presented to demonstrate the capabilities of the system as CAD tools for some chest diseases like congestive heart failure, lung collapse, etc. Experimental result obtained with an algorithm to detect early nodules for lung cancer and TP is very encouraging. Data mining and other artificial intelligent techniques may be used to make the system becoming more active and powerful expert system

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Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses

Cited by 16 publications

References 10 publications

Tumor detection and classification in breast mammography based on fine-tuned convolutional neural networks

Tumor detection and classification in breast mammography based on fine-tuned convolutional neural networks

A Neuro-Fuzzy Inference Model for Breast Cancer Recognition

Chest X-ray analysis for an active distributed E-health system with computer-aided diagnosis

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