Using Artificial Immune Recognition Systems in Order to Detect Early Breast Cancer

Katsis, Christos D.; Gkogkou, I.; Papadopoulos, Chris; Goletsis, Yorgos; Boufounou, Paraskevi

doi:10.5815/ijisa.2013.02.04

Cited by 12 publications

(11 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An example of such methods is developed in [16]. Multiple modalities, like mammography, ultrasonography and magnetic resonance imaging, can also be used [17]. More details for different imaging modalities are given in a review established by [18].…”

Section: Related Workmentioning

confidence: 99%

“…For breast cancer diagnosis, many developed AIRS based researches show very important accuracies on Wisconsin breast cancer dataset [45] [46]. AIRS was also applied by Katsis et al [17] to detect early breast cancer using different examinations (i.e. mammography, ultrasonography and magnetic resonance imaging) with promising results.…”

Section: Artificial Immune Recognition Systemmentioning

confidence: 99%

See 1 more Smart Citation

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Belkhodja¹,

Hamdadou²

2019

IJIMAI

View full text Add to dashboard Cite

Computer aided systems for detection and diagnosis on mammograms are one of the automatic solutions that help the radiologist in detecting abnormalities in an efficient way as a second reader of digital mammograms. To this end, we propose, in this paper, a methodology for computeraided detection of breast masses on screening mammograms, which joins multidisciplinary axes such as medical domain, image processing and biological pattern recognition. For this, we focus on minimizing false positive findings and increasing true positive cases using all benefits of fuzzy processing and artificial immune recognition system.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Artificial Immune Recognition Systemmentioning

confidence: 99%

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Belkhodja¹,

Hamdadou²

2019

IJIMAI

View full text Add to dashboard Cite

show abstract

“…This paper shows both merits and demerits of machine learning techniques like SVM, Naïve Bayes, Neural Network.C.D. Katsis in et al [3] proposed a way using Correlation Feature Selection (CFS) procedure & an Artificial Immune Recognition System (AIRS) classifier for breast cancer prediction. By adopting the SVM technique the accuracy results as 76.33% on data collected for 52 patients among 4726 cases.…”

Section: Related Studymentioning

confidence: 99%

Usage of Data Mining Techniques in Predicting the Heart Diseases Decision Tree & Random Forest Algorithm

Rao*,

Anitha

2019

IJITEE

View full text Add to dashboard Cite

Nowadays, heart disease is the main cause of several deaths among all other diseases. Due to the lack of resources in the medical field, the prediction of heart diseases becomes a major problem. For early diagnosis and treatment, some classification algorithms such as Decision Tree and Random Forest Algorithm are used. The data mining techniques compare the accuracy of the algorithm and predict heart diseases. The main aim of this paper is to predict heart disease based on the dataset values. In this paper we are comparing the accuracy of above two algorithms. To implement these methods the following steps are used. In first phase, a dataset of 13 attributes is collected and it was applied on classification techniques using the Decision tree and Random Forest Algorithms. Finally, the accuracy is collected for both the algorithms. In this paper we observed that random forest is generating better results than decision tree in prediction of heart diseases.

show abstract

“…From the study of the earlier research work on drifting concepts, data streams are said to form continuous flow of data which is accessible only "once" an arrival, after that the data is lost and new data arrives, which may have a different concept. This changing data concept is being widely used now-adays in large number of applications like "Market-Basket analysis [12,14] " , web data, computer security, information filtering, medical diagnosis [35] etc.…”

Section: Introductionmentioning

confidence: 99%

Empirical Support for Concept Drifting Approaches: Results Based on New Performance Metrics

Sidhu¹,

Bhatia²

2015

IJISA

View full text Add to dashboard Cite

Abstract-Various types of online learning algorithms have been developed so far to handle concept drift in data streams. We perform more detailed evaluation of these algorithms through new performance metrics -prequential accuracy, kappa statistic, CPU evaluation time, model cost, and memory usage. Experimental evaluation using various artificial and real-world datasets prove that the various concept drifting algorithms provide highly accurate results in classifying new data instances even in a resource constrained environment, irrespective of size of dataset, type of drift or presence of noise in the dataset. We also present empirically the impact of various features-size of ensemble, period value, threshold value, multiplicative factor and the presence of noise on all the key performance metrics.

show abstract

Using Artificial Immune Recognition Systems in Order to Detect Early Breast Cancer

Cited by 12 publications

References 34 publications

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Usage of Data Mining Techniques in Predicting the Heart Diseases Decision Tree & Random Forest Algorithm

Empirical Support for Concept Drifting Approaches: Results Based on New Performance Metrics

Contact Info

Product

Resources

About