Lung Cancer Stage Identification Using Enhanced Model from Deep Learning
Keywords:
Deep Learning (DL), Convolutional Neural Network (CNN), Hybrid Algorithm, Image Processing, Stage Identification, Medical Imaging.Abstract
Ddigital image processing techniques such as
classification and segmentation are now widely applied
in the medical area with the aim of early detection of
diseases. From the lungs Computed Tomography (CT)
scan images the images are pre-processing and the
Region of Interest (ROI) is segmented. In this work
trying to develop CNN algorithm for Lung Cancer
detection from CT-SCAN images and for CNN training.
We use CT-SCAN images dataset which is downloaded
from Kaggle.com website. This application uses hybrids
algorithm of CNN-LSTM to enhance the performance
of lung cancer prediction. The first step is to build a
hybrid model using 80% of the x-ray images of lung
cancer and the second step is to use the remaining 20%
of the data to classify lung cancer. Present in the CT
scan are of two types’ Normal’ and ‘ABNORMAL’. If
the combined method (LSTM and CNN) will be used,
accuracy, precision, fscore and recall will be developed
far better than CNN. CNN algorithm is obtained 97%
accuracy while hybrid CNN and LSTM has above 98%
accuracy which is superior to CNN algorithm.
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