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Deep Convolutional Neural Networks in Detecting Lung Mass From Chest X-Ray Images
Abstract
There are more than one million cases of lung cancer per year in India alone. Early detection is vital in increasing the survival rate and decreasing treatment costs. This research is aimed at building a deep convolutional neural network which uses chest x-rays to identify lung mass, and then make a comparative study by tuning the hyperparameters. NIH Chest X-Ray Dataset containing more than 112,000 images were used for training and testing. The data was analysed and then fed to the neural network. Accuracy of over 96% was obtained in all the trials. A comparative study by varying the number of inputs and varying the number of hidden layers was carried out. The accuracies obtained were compared and was found that the accuracy increased with the increase in the number of hidden layers. A complete product was then ideated which when implemented would be a vital diagnostic tool and can be used in the remote locations of a country having just x-ray facilities and no other advanced medical equipment like CT.
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