Estimation of Level of Liver Damage Due to Cancer using Deep Convolutional Neural Network in CT images
Swapnil V. Vanmore1, Sangeeta R. Chougule2

1Mr. Swapnil V. Vanmore , Research Scholar, Department Electronics &Telecommunication Engineering. Sanjeevan Engineering Technology & Institute, Panhala .Shivaji University Kolhapur, India
2Dr. S. R. Chougule , Department Electronics &Telecommunication Engineering, Kolhapur Institute of Technology College of Engineering Kolhapur, India

Manuscript received on October 11, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 3761-3764 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4818119119/2019©BEIESP | DOI: 10.35940/ijitee.A4818.119119
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Abstract: The lesion size estimation is essential need while diagnosing the liver cancer and treatment scenario. The lesion segmentation suing conventional methods such as region growing, threshold based segmentation provide limited performance due to variations in light intensity distribution throughout the image. The deep learning approach used in this paper consist of input dataset of liver abdominal images along with labelled set combination of variety of liver regions and lesion structures. The care has been taken while constructing the dataset such that, the lesion due to cancer in liver of particular image should have at least one matching structure should be present in one of the labelled images. The 3 fold validation is done to evaluate the performance in which total 140 images of liver cancer are used for training, 30 images for validation and 30 images for testing. The result shows 98.5% accuracy for lesion classification. The area of lesion is compared to total area of liver in terms of pixels to estimate the total area occupied by the lesion and amount of liver damage.
Keywords: Liver Cancer, Medical Image Segmentation, Neural Network, Lesion Segmentation, Deep Learning.
Scope of the Article: Deep Learning