Lung Cancer Image Classification system using Random Forest Classifier

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.8 · Issue 2

Published

September 2019

Access

Open Access

Licence

CC BY-NC-SA 4.0

Sudeep Gujar Laal Singh Chauhan Nisha Kumawat

Lung cancer is a real public health problem. Indeed, it is the leading cause of cancer mortality in the world, survival at 5 years is only 15% and this is largely due to late diagnosis and high metastatic power. Improving the management of this type of cancer, therefore, implies better knowledge of the processes of oncogenesis and tumor invasion. Most of the models for lung cancer classification based on lung cancer images are various types of classification model with binarization image pre-processing. This paper proposes a method based on a Random forest classifier for lung cancer image classification from the given database images. Feature extraction of the image is accomplished using Gabor Wavelet and GLCM (Grey Level Co-occurrence Matrix). Then the extracted features are classified by the Random forest classifier. This paper provides the confusion matrix with sensitivity, specificity, and accuracy for Gabor wavelet, GLCM and Hybrid (Gabor + GLCM) based approaches.

Lung Cancer Gabor Wavelet GLCM Random Forest Classifier

Sudeep Gujar, Laal Singh Chauhan, Nisha Kumawat (2019). Lung Cancer Image Classification system using Random Forest Classifier. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.8, Issue 2. ISSN: 2319-4863.

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