RETRACTED: Deep learning based two-fold segmentation model for liver tumor detection

Abstract

Liver Tumour (LT) develops when healthy cells undergo abnormal DNA changes that cause them to grow and divide uncontrollably. In manual examination, evaluation might be changed by the unique perception of the observers, which depends on their expertise and subjectivity. Therefore, computer-aided intelligent tools are established to eliminate subjectivity and increase the performance. To overcome these challenges, a novel Two-fold Segmentation of Liver Tumour (TFSLT) model for accurately detecting the liver tumour using computed tomography (CT) images. Initially, the CT images are pre-processed using Normalized-Modified Anisotropic Diffusion Filtering (NMADF) Algorithm to reduce the noise artifacts. These pre-processed CT images are taken as input to the Canny Edge Detector (CED) for detecting the edges of the liver. Based on these edges, the first-fold segmentation process is performed using the Jaccard metric-based Watershed (JMWS) algorithm to accurately segment the liver region. Improved Deep Neural Network (IDNN) is utilized to classify the LT into normal, Hepatocellular carcinoma (HCC), Cholangio carcinoma (CC) and Metastatic tumour (MT). Modified Elephant Herd Optimization (MEHO) algorithm for the MEHO algorithm for selecting the features of the images. Finally, the Improved Expectation-Maximization (IEM) Algorithm as second-fold segmentation process to segment the different abnormal classes. The performance of the proposed TFSLT approach is assessed using the specific metrics like recall, precision, specificity, accuracy and F1 score. The experimental findings reveal that the proposed TFSLT approach achieves a better accuracy range of 99.57% for detecting LT in its early stages.

A Combined Ensemble Model (CEM) for a Liver Cancer Detection System

“…Finally, the DNN is used for classification based on segmentation outcomes. Anandan et al [35] presented the enhanced filtering approach called NMADF that helps filter the input CT scan images. The proposed approach uses the two-fold segmentation that segments the liver cancer images.…”

Analysis of the Influence of Number of Segments on Similarity Level in Wound Image Segmentation Using K-Means Clustering Algorithm

“…The use of noise and filters is based on measuring image quality values. There are four image quality measurements, namely Mean Square Error (MSE) [12]- [13], Peak Signal Noise Ratio (PSNR) [14]- [16], Variation of Information (VOI) [17]- [18], and Global Consistency Error (GCE) [19]- [21]. The smaller the MSE value, the better the recovery value from the noise effect, and in PSNR the higher the value, the better because it is considered close to the original image.…”