Contrast Enhancement based CNN model for Lung Cancer Classification and Prediction using Chest X-ray Images

Abstract

Lung Cancer is one among the most perilous disease caused by various reasons with smoking being the common factor across the globe. Early detection is best for treating any type of cancer and this is very much true even with lung cancer. However, in Indian scenario, a patient approaching medical diagnosis at the early stage is quite rare. By the time first screening is done, cancer would have been grown to Grade 2 or higher level. Smoking and consuming tobacco products, as well as exposure to second-hand smoke are said to be major reason for this lung cancer. Classifying the given X-ray into cancerous and non-cancerous is challenging problem. Most of the literature’s reported so far have explored many deep neural network models for classifying the chest X-ray images in binary classification such as cancerous and non-cancerous. However, Chest X-rays are observed to have poor contrast in some cases, enhancing this contrast prior to training could be beneficial in terms of better accuracy of the model. Hence in this paper we present novel method of gamma corrected based CNN model for chest X-ray images classification. The proposed model has highest accuracy that is 0.92 and compared to other recently reported literature’s, our model is performing slightly better.