BFO – AIS: A Framework for Medical Image Classification Using Soft Computing Techniques

D. Chitra and M. Karthikeyan, Govt. Arts College (A), India

ABSTRACT

Medical images provide diagnostic evidence/information about anatomical pathology. The growth in database is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI), Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work. CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio therapy. Medical information systems goals are to deliver information to right persons at the right time and place to improve care process quality and efficiency. This paper proposes an Artificial Immune System (AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO) with Local Search (LS) for medical image classification.

KEYWORDS

Computed Tomography (CT), Feature Selection, Artificial immune classifier, Correlation based Feature Selection (CFS), Bacterial Foraging Optimization (BFO), Local Search (LS) 

Original Source URL: https://aircconline.com/ijsc/V8N1/8117ijsc02.pdf

https://airccse.org/journal/ijsc/current2017.html

Evolving Efficient Clustering and Classification Patterns in Lymphography Data through Data Mining Techniques

Shomona Gracia Jacob1 and R.Geetha Ramani2, 1Rajalakshmi Engineering College, India, 2Anna University, Chennai, India

ABSTRACT

Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. Clustering and Classification are two distinct phases in data mining that work to provide an established, proven structure from a voluminous collection of facts. A dominant area of modern-day research in the field of medical investigations includes disease prediction and malady categorization. In this paper, our focus is to analyze clusters of patient records obtained via unsupervised clustering techniques and compare the performance of classification algorithms on the clinical data. Feature selection is a supervised method that attempts to select a subset of the predictor features based on the information gain. The Lymphography dataset comprises of 18 predictor attributes and 148 instances with the class label having four distinct values. This paper highlights the accuracy of eight clustering algorithms in detecting clusters of patient records and predictor attributes and highlights the performance of sixteen classification algorithms on the Lymphography dataset that enables the classifier to accurately perform multi-class categorization of medical data. Our work asserts the fact that the Random Tree algorithm and the Quinlan’s C4.5 algorithm give 100 percent classification accuracy with all the predictor features and also with the feature subset selected by the Fisher Filtering feature selection algorithm.. It is also stated here that the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm offers increased clustering accuracy in less computation time.

KEYWORDS

Data mining, Clustering, Feature Selection, Classification, Lymphography Data 

Original Source URL: https://airccse.org/journal/ijsc/papers/3312ijsc09.pdf

https://airccse.org/journal/ijsc/current2012.html

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BFO – AIS: A Framework for Medical Image Classification Using Soft Computing Techniques

D. Chitra and M. Karthikeyan

Assistant Professor, Dept. of Computer Science, Govt. Arts College (A), Salem-7, Tamil Nadu, India

ABSTRACT

Medical images provide diagnostic evidence/information about anatomical pathology. The growth in database is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI), Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work. CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio therapy. Medical information systems goals are to deliver information to right persons at the right time and place to improve care process quality and efficiency. This paper proposes an Artificial Immune System (AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO) with Local Search (LS) for medical image classification.

KEYWORDS

Computed Tomography (CT), Feature Selection, Artificial immune classifier, Correlation based Feature Selection (CFS), Bacterial Foraging Optimization (BFO), Local Search (LS) 

Original Source URL: https://aircconline.com/ijsc/V8N1/8117ijsc02.pdf

http://airccse.org/journal/ijsc/current2017.html

EVOLVING EFFICIENT CLUSTERING AND CLASSIFICATION PATTERNS IN LYMPHOGRAPHY DATA THROUGH DATA MINING TECHNIQUES

EVOLVING EFFICIENT CLUSTERING AND CLASSIFICATION PATTERNS IN LYMPHOGRAPHY DATA THROUGH DATA MINING TECHNIQUES 

Shomona Gracia Jacob1 and R.Geetha Ramani2 1Department of Computer Science and Engineering, Rajalakshmi Engineering College (Affiliated to Anna University, Chennai) graciarun@gmail.com 2Department of Information Science and Technology, College of Engineering, Guindy, Anna University, Chennai. rgeetha@yahoo.com

ABSTRACT 

Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. Clustering and Classification are two distinct phases in data mining that work to provide an established, proven structure from a voluminous collection of facts. A dominant area of modern-day research in the field of medical investigations includes disease prediction and malady categorization. In this paper, our focus is to analyze clusters of patient records obtained via unsupervised clustering techniques and compare the performance of classification algorithms on the clinical data. Feature selection is a supervised method that attempts to select a subset of the predictor features based on the information gain. The Lymphography dataset comprises of 18 predictor attributes and 148 instances with the class label having four distinct values. This paper highlights the accuracy of eight clustering algorithms in detecting clusters of patient records and predictor attributes and highlights the performance of sixteen classification algorithms on the Lymphography dataset that enables the classifier to accurately perform multi-class categorization of medical data. Our work asserts the fact that the Random Tree algorithm and the Quinlan’s C4.5 algorithm give 100 percent classification accuracy with all the predictor features and also with the feature subset selected by the Fisher Filtering feature selection algorithm.. It is also stated here that the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm offers increased clustering accuracy in less computation time. 

KEYWORDS 

Data mining, Clustering, Feature Selection, Classification, Lymphography Data   

ORIGINAL SOURCE URL : http://airccse.org/journal/ijsc/papers/3312ijsc09.pdf

http://airccse.org/journal/ijsc/current2012.html