A Fuzzy-Membrane-Immune Algorithm For Breast Cancer Diagnosis (original) (raw)
Related papers
A Hybrid Artificial Immune Genetic Algorithm with Fuzzy Rules for Breast Cancer Diagnosis
2008
The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we give an introduction to fuzzy systems, genetic algorithms and artificial immune system, and then we introduce a hybrid algorithm that gathers the genetic algorithms with the artificial immune system in one algorithm. The genetic algorithm, the artificial immune system and the hybrid algorithm were implemented and tested on the Wisconsin breast cancer diagnosis (WBCD) problem in order to generate a fuzzy rule system for breast cancer diagnosis. The hybrid algorithm generated a fuzzy system which reached the maximum classification ratio earlier than the two other ones. The motivations of using fuzzy rules incorporate with evolutionary algorithms in the underline problem are attaining high classification performance with the possibility of attributing a confidence measure (degree of benignity or malignancy) to the output diagnosis beside the simplicity of the diagnosis system which means that the system is human interpretable.
Computers in Biology and Medicine, 2007
The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k-nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too.
Computers in Biology and Medicine, 2007
The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k-nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too.
A Neuro-Fuzzy Based System for the Classification of Cells as Cancerous or Non-Cancerous
International Journal of Medical Research and Health Sciences, 2018
Objectives: In this study, we developed a neuro-fuzzy based system for classification of cancerous and non-cancerous lung cells. Methods: Images were pre-processed using median filter algorithm, segmented using marker-controlled watershed algorithm, and were extracted using gray-level co-occurrence matrix. A hybridized diagnosis system that made use of neural network and fuzzy logic for classification of lung cells into cancerous and non-cancerous cells is modelled. Computed tomography (CT) scan image dataset of the lung was downloaded from The Cancer Imaging Archive dataset. Neural network performed the training and classification of the lung cells with back-propagation algorithm, while the cancerous cells were passed into fuzzy inference system to determine the lung cancer stage. Results: Our system was able to successfully classify the imported CT scan images into normal or abnormal with considerably high accuracy of 70% and precision of 89%. This system can support physicians in decision making when diagnosing cancer.
Breast Cancer Diagnosis Using Genetic Fuzzy Rule Based System
Breast cancer diagnosis (WBCD) is an important, real-world medical problem. There are different artificial Intelligence techniques try to classify WBCD to help to minimize the errors that might occur when the doctors do not have adequate experience or because of stress . In this work , fuzzy genetic tool is used to present diagnostic system that classify WBCD cases automatically .The system provides two prime features: first, it attain high classification performance ; second, the resulting system consists of a few simple rules, and are therefore interpretable.
Diagnosing breast cancer with an improved artificial immune recognition system
Soft Computing, 2015
Breast cancer is the top cancer in women worldwide. Scientists are looking for early detection strategies which remain the cornerstone of breast cancer control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose this disease. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. To increase the classification accuracy, this study introduces a new hybrid system that incorporates support vector machine, fuzzy logic, and real tournament selection mechanism into AIRS. The Wisconsin Breast Cancer data set was used as the benchmark data set; it is available through the machine learning repository of the University of California at Irvine. The classification performance was measured through tenfold cross-validation, student's t test, sensitivity Communicated by V. Loia.
A fuzzy-genetic approach to breast cancer diagnosis
Artificial Intelligence in Medicine, 1999
The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies-fuzzy systems and evolutionary algorithms -so as to automatically produce diagnostic systems. We find that our fuzzy-genetic approach produces systems exhibiting two prime characteristics: first, they attain high classification performance (the best shown to date), with the possibility of attributing a confidence measure to the output diagnosis; second, the resulting systems involve a few simple rules, and are therefore (human-) interpretable. (C.A. Peñ a-Reyes), moshe.sipper@di.epfl.ch (M. Sipper) 0933-3657/99/$ -see front matter © 1999 Elsevier Science B.V. All rights reserved. PII: S 0 9 3 3 -3 6 5 7 ( 9 9 ) 0 0 0 1 9 -6 C.A. Peña-Reyes, M. Sipper / Artificial Intelligence in Medicine 17 (1999) 131-155 132
Expert Systems With Applications, 2007
Artificial Immune Recognition System (AIRS) classification algorithm, which has an important place among classification algorithms in the field of Artificial Immune Systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including Breast Cancer, Cleveland Heart Disease, Diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by Fuzzy-Logic. This system, named as Fuzzy-AIRS was used as a classifier in the diagnosis of Breast Cancer and Liver Disorders, which are of great importance in medicine. The classifications of Breast Cancer and BUPA Liver Disorders datasets taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. Fuzzy-AIRS, which reached to classification accuracy of 98.51% for breast cancer, classified the Liver Disorders dataset with 83.36% accuracy. For both datasets, Fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, Fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. In the experiments, it was seen that the classification time in Fuzzy-AIRS was reduced about 70% of AIRS for both datasets. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, Fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems.
A Fuzzy-Genetic Algorithm Method for the Breast Cancer Diagnosis Problem
2015
The computer-aided medical diagnosis of complex systems, such as breast cancer is an important medical problem. In this paper, we focus on combining two major methodologies, namely, the fuzzy-based systems and the evolutionary genetic algorithms to find a computer aided diagnosis system that will aid physicians in an early diagnosis of breast cancer in Saudi Arabia. Our results show that the fuzzy-genetics approach produces systems that attain high classification performance, with simple and well interpretive rules and a good degree of