Hybrid Deep Learning Method for Detection of Liver Cancer

  • Sunita P. Deshmukh Department of Electronics and Telecommunication, GHRU Amaravati, Amaravati, India
  • Dharmaveer Choudhari Department of Electronics and Telecommunication, GHRU Amaravati, Amaravati, India
  • Shankar Amalraj Department of Electronics and Telecommunication, GHRU Amaravati, Amaravati, India
  • Pravin N. Matte Department of Electronics and Telecommunication, GHRU Amaravati, Amaravati, India


Liver disease refers to any liver irregularity causing its damage. There are several kinds of liver ailments. Benign growths are rarely life threatening and can be removed by specialists. Liver malignant tumor is leading causes of cancer death. Identifying malignant growth tissue is a troublesome and tedious task. There is significantly less information and statistical analysis presented related to cholangiocarcinoma and hepatoblastoma. This research focuses on the image analysis of these two types of cancer. The framework’s performance is evaluated using 2871 images, and a dual hybrid model is used to accomplish superb exactness. The aftereffects of both neural networks are sent into the result prioritizer that decides the most ideal choice for image arrangement. The relevance of elements appears to address the appropriate imaging rules for each class, and feature maps matching the original picture voxel features. The significance of features represents the most important imaging criteria for each class. This deep learning system demonstrates the concept of illuminating elements of a pre-trained deep neural network’s decision-making process by an examination of inner layers and the description of attributes that contribute to predictions.


liver cancer detection, deep learning, fully convolutional neural network, hybrid approach, discrete wavelet transform (DWT),


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Mar 1, 2023
How to Cite
DESHMUKH, Sunita P. et al. Hybrid Deep Learning Method for Detection of Liver Cancer. Computer Assisted Methods in Engineering and Science, [S.l.], p. Article no. 463, mar. 2023. ISSN 2956-5839. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/463>. Date accessed: 21 mar. 2023. doi: http://dx.doi.org/10.24423/cames.463.
[CLOSED]Scientific Computing and Learning Analytics for Smart Healthcare Systems