Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework

Abstract

Liver cancer is one of the world's largest causes of death to humans. It is a difficult task and time consuming to identify the cancer tissue manually in the present scenario. The segmentation of liver lesions in CT images can be used to assess the tumor load, plan treatments predict, and monitor the clinical response. In this paper, the Hybridized Fully Convolutional Neural Network (HFCNN) has been proposed for liver tumor segmentation, which has been modeled mathematically to resolve the current issue of liver cancer. For semantic segmentation, HFCNN has been used as a powerful tool for liver cancer analysis. Whereas the CT-based lesion-type definition defines the diagnosis and therapeutic strategy, the distinction between cancer and non-cancer lesions is crucial. It demands highly qualified experience, expertise, and resources. However, a deep end-to-end learning approach to help discrimination in abdominal CT images of the liver between liver metastases of colorectal cancer and benign cysts has been analyzed. Our method includes the successful extraction of features from Inception combined with residual and pre-trained weights. Feature maps have been consistent with the original image voxel features, and The importance of features seemed to represent the most relevant imaging criteria for every class. This deep learning system shows the concept of illumination portions of the decision-making process of a pre-trained deep neural network, through an analysis of inner layers and the description of features that lead to predictions.

Practical utility of liver segmentation methods in clinical surgeries and interventions

“…The result highlights a Jaccard distance error of 14.4 ± 5.3% and a relative volume difference of -8.1 ± 2.1% on a custom CT dataset with 18 patients. Recently, Dong et al [ 91 ] propose a Hybridized Fully Convolutional Neural Network (HFCNN) to detect cancer and segment liver tumors.…”

Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

“…Similarly, a total of 147 images are taken for testing in which each class has 21 images. Also, the efficiency of these classifier models is compared with DNN [9], CADx [11], MLP [13], CNN [16], and MIL [17] in terms of precision, recall, fmeasure, and accuracy.…”

“…Dong et al [13] recommended the Hybridized Fully CNN (HFCNN) to segment and predict liver cancer from CT images.…”

Practical Utility of Liver Segmentation Techniques in Clinical Surgeries and Interventions

“…Balagourouchetty et al [22] suggest an ensemble FCNet classifier trained using GoogLeNet features to classify six different classes of liver tumors. Recently, Dong et al [83] proposed a Hybridized Fully Convolutional Neural Network (HFCNN) to detect cancer and segment liver tumors.…”