CONVOLUTIONAL NEURAL NETWORK BASED ON CUCKOO SEARCH FOR CANCER DETECTION BASED ON MICROARRAY DATA
Cancer is one of deadly disease in the world and needed to detect the symptoms early. Cancer can be represented with microarray data with measuring the changes occured in gene expression level. Cancer detection can be done by doing classification technique for microarray data. One of method that...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/59637 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Cancer is one of deadly disease in the world and needed to detect the symptoms early.
Cancer can be represented with microarray data with measuring the changes occured
in gene expression level. Cancer detection can be done by doing classification
technique for microarray data. One of method that applied for classification is Deep
Learning like Convolutional Neural Network (CNN). It has given impact in
classification but it is sensitive to noise data. Microarray data has a large features
(high dimensional) which is not all the features has important information (high noise)
and small samples which is causing the classification is difficult and affect the
accuracy. Cuckoo Search (CS) is one of search optimization algorithm that could find
the optimal feature. The purpose of this research is to implement and analyze the effect
of feature selection and classification on microarray data using CS as feature selection
and CNN as classifier. By applying CS as a selection feature and CNN as a classifier,
we are able to find the most significant features. The effect of feature selection on plays
an important role in avoiding noise data accurately predicting classification. |
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