Abstract
Cancer has been recognized by the World Health Organization as the second leading reason for deaths around the world. With the rise in population, Hepatocellular Carcinoma (HCC) cases have increased due to a lack of early diagnosis and treatment. Conventionally, CT or MRI scans of affected livers undergo manual examination by trained professionals, which usually takes substantial time and effort. With the rising number of cases, this process needs to be sped up. Using deep learning models for medical image segmentation has proven to be an effective method. The proposed approach of deep learning model uses a 2D U-net architecture constructed on fully convolutional network (FCN). The U-net architecture consists of three layers; the contracting/down-sampling, the expanding/up-sampling, and the bottleneck layer which acts as a median between the other two layers. The dataset consists of computed tomography images for training and testing respectively where each scan is in a 3D image format called NIfTI (.nii) and is of variable sizes. Our proposed model is enveloped in application software, where the front end provides a minimalist and intuitive user experience. Using this approach, we received an accuracy of 0.71 using the dice similarity metric. The main benefit of having an application software approach is the ease of adoption in places where such a solution is required to save valuable time and effort.
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Acknowledgements
Our project received a financial assistance from Kerala State Council for State, Technology and Environment (KSCSTE), as a part of their Student Project scheme.
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Abhijith, V., Biju, M., Gopakumar, S., Gomez, S.A., Mathew, T. (2021). Automated Detection of Liver Tumor Using Deep Learning. In: Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 736. Springer, Singapore. https://doi.org/10.1007/978-981-33-6987-0_35
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DOI: https://doi.org/10.1007/978-981-33-6987-0_35
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