Hybrid Deep Learning Method for Detection of Liver Cancer

Sunita P. Deshmukh; Dharmaveer Choudhari; Shankar Amalraj; Pravin N. Matte

doi:10.24423/cames.463

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

DOI: http://dx.doi.org/10.24423/cames.463

Abstract

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.

Keywords

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

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