Journal ArticleDOI
A Hybrid Scheme for Breast Cancer Detection using Intuitionistic Fuzzy Rough Set Technique
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TLDR
This paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques and shows the overall accuracy of 98.3% is higher than the accuracy achieved by hybridizing fuzzy rough set model.Abstract:
Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques. The hybrid scheme starts with image segmentation using intuitionistic fuzzy set to extract the zone of interest and then to enhance the edges surrounding it. Further feature extraction using gray-level co-occurrence matrix is presented. Additionally, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. The experimental analysis shows the overall accuracy of 98.3% and it is higher than the accuracy achieved by hybridizing fuzzy rough set model.read more
Citations
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Journal ArticleDOI
A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU
Sweta Bhattacharya,Siva Rama Krishnan S,Praveen Kumar Reddy Maddikunta,Rajesh Kaluri,Saurabh Singh,Thippa Reddy Gadekallu,Mamoun Alazab,Usman Tariq +7 more
TL;DR: A hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets and experimental results confirm the fact that the proposed model performs better than the existing machine learning models.
Journal ArticleDOI
Segmentation and Feature Extraction in Medical Imaging: A Systematic Review
TL;DR: Authors survey on various segmentation and feature extraction methods in medicinal images used for preprocessing in order to find the inner or outer construction of mortal body.
Journal ArticleDOI
An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm
Chiranji Lal Chowdhary,Mohit Mittal,P. Kumaresan,Priyadarshini Adyasha Pattanaik,Zbigniew Marszalek +4 more
TL;DR: The proposed approach is highly effective with clustering and also with classification of breast cancer and has been compared with other available fuzzy clustering methods to prove the efficacy.
Journal ArticleDOI
Clustering Algorithm in Possibilistic Exponential Fuzzy C-Mean Segmenting Medical Images
TL;DR: It was concluded that the possibilistic exponential fuzzy c-means segmentation algorithm endorsed for additional efficient for accurate detection of breast tumours to assist for the early detection.
Journal ArticleDOI
Hybrid Firefly-Bat Optimized Fuzzy Artificial Neural Network Based Classifier for Diabetes Diagnosis
Gadekallu Reddy,Neelu Neelu +1 more
TL;DR: This paper introduces a technique known as FFBAT-ANN prediction algorithm which is categorized as Feature reduction and Diabetes disease classification and such a process is carried out using LPP algorithm and FFBAT artificial neural network classifier respectively.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI
Intuitionistic fuzzy sets
TL;DR: Various properties are proved, which are connected to the operations and relations over sets, and with modal and topological operators, defined over the set of IFS's.
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Rough sets
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Journal ArticleDOI
A fuzzy-genetic approach to breast cancer diagnosis.
TL;DR: This paper focuses on the Wisconsin breast cancer diagnosis problem, combining two methodologies-fuzzy systems and evolutionary algorithms-so as to automatically produce diagnostic systems, finding that the fuzzy-genetic approach produces systems exhibiting two prime characteristics.