LUNG DISEASES IDENTIFICATION WITH HYBRID GENETIC- MLP APPROACH

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Published: 2016-01-15

Page: 164-172


RAMANDEEP KAUR *

Department of Computer Science and Engineering, CT Institute of Engg. Mgt. & Tech, Jalandhar, India

PRINCE VERMA

Department of Computer Science and Engineering, CT Institute of Engg. Mgt. & Tech, Jalandhar, India

*Author to whom correspondence should be addressed.


Abstract

Data Mining is the process of extracting or mining information from the large volumes of data. Classification is a data mining task, used to classify data among different predefined clusters. The paper proposes a new classifier utilizing MLP approach by maintaining the clusters based on similar feature with distance formula. With using cluster based method MLP approach can handle large data and increases the accuracy. This technique has been applied for automating medical diagnosis. This paper analyzes the lung images (i.e. CT-scan images) for identifying and classifying them among the various lung diseases (i.e. bronchitis, emphysema, pleural effusion or normal) by emphasizing on the problematic area.

 

Keywords: Data mining, classification, multilayer perceptron


How to Cite

KAUR, RAMANDEEP, and PRINCE VERMA. 2016. “LUNG DISEASES IDENTIFICATION WITH HYBRID GENETIC- MLP APPROACH”. Journal of International Research in Medical and Pharmaceutical Sciences 8 (4):164-72. https://ikprress.org/index.php/JIRMEPS/article/view/2305.

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