Abstract
GENETIC BEE OPTIMIZED RECURRENT NETWORK BASED LUNG CANCER DETECTION
P. Sankar Ganesh, Teja, Marish and Dr. K. Devi*
ABSTRACT
Lung cancer is one of the serious issue in healthcare application which occurs in smoking people. The lung cancer occurred in types such as small cell and non-small cell lung cancer. It also created by exposure of toxin gases, family history, second-hand smoke etc. The disease requires immediate treatment to avoid the serious issue. Therefore, several researcher?s focus on the automatic cancer detection system; however the existing systems fails to concentrate the exact disease affected region prediction. This causes to reduce the efficiency of cancer prediction accuracy. To overcome this issues, in this, genetic bee optimized recurrent network is applied to improve the overall prediction accuracy. Initially, the images are collected from Chest CT-Scan images Dataset which is processed by median filter to removing noise. Then, dual clustering approach is applied to segment the affected region which helps to identify the cancer related features. The extracted features are classified using recurrent approach that uses the optimization algorithm to regularize the network performance. The effectiveness of the system is evaluated using MATLAB based results.
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