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. 2022 Jun 30:2022:6162445.
doi: 10.1155/2022/6162445. eCollection 2022.

Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification

Mesfer Al Duhayyim  1 , Hanan Abdullah Mengash  2 , Radwa Marzouk  2 , Mohamed K Nour  3 , Hany Mahgoub  4   5 , Fahd Althukair  6 , Abdullah Mohamed  7
Affiliations

Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification

Mesfer Al Duhayyim et al. Comput Intell Neurosci. .

Retraction in

Abstract

Biomedical engineering is the application of the principles and problem-solving methods of engineering to biology along with medicine. Computation intelligence is the study of design of intelligent agents which are systems acting perceptively. The computation intelligence paradigm offers more advantages to the enhancement and maintenance of the field of biomedical engineering. Liver cancer is the major reason of mortality worldwide. Earlier-stage diagnosis and treatment might increase the survival rate of liver cancer patients. Manual recognition of the cancer tissue is a time-consuming and difficult task. Hence, a computer-aided diagnosis (CAD) is employed in decision making procedures for accurate diagnosis and effective treatment. In contrast to classical image-dependent "semantic" feature evaluation from human expertise, deep learning techniques could learn feature representation automatically from sample images using convolutional neural network (CNN). This study introduces a Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification (HRO-DLBLCC) model. The proposed HRO-DLBLCC model majorly focuses on the identification of liver cancer in the medical images. To do so, the proposed HRO-DLBLCC model employs preprocessing in two stages, namely, Gabor filtering (GF) based noise removal and watershed transform based segmentation. In addition, the proposed HRO-DLBLCC model involves NAdam optimizer with DenseNet-201 based feature extractor to generate an optimal set of feature vectors. Finally, the HRO algorithm with recurrent neural network-long short-term memory (RNN-LSTM) model is applied for liver cancer classification, in which the hyperparameters of the RNN-LSTM model are tuned by the use of HRO algorithm. The HRO-DLBLCC model is experimentally validated and compared with existing models. The experimental results assured the promising performance of the HRO-DLBLCC model over recent approaches.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Block diagram of HRO-DLBLCC technique.
Figure 2
Figure 2
Layered structure in DenseNet-201.
Figure 3
Figure 3
Sample images.
Figure 4
Figure 4
Confusion matrices of HRO-DLBLCC technique: (a) entire dataset, (b) 70% of TR data, and (c) 30% of TS data.
Figure 5
Figure 5
Result analysis of HRO-DLBLCC algorithm under entire dataset.
Figure 6
Figure 6
Result analysis of HRO-DLBLCC algorithm under 70% of TR data.
Figure 7
Figure 7
Result analysis of HRO-DLBLCC algorithm under 30% of TS data.
Figure 8
Figure 8
TA and VA analysis of HRO-DLBLCC algorithm.
Figure 9
Figure 9
TL and VL analysis of HRO-DLBLCC algorithm.
Figure 10
Figure 10
Precision-recall curve analysis of HRO-DLBLCC algorithm.
Figure 11
Figure 11
ROC curve analysis of HRO-DLBLCC algorithm.
Figure 12
Figure 12
Accu y analysis of HRO-DLBLCC algorithms with existing methodologies.
Figure 13
Figure 13
prec n analysis of HRO-DLBLCC algorithms with existing methodologies.
Figure 14
Figure 14
Recal analysis of HRO-DLBLCC algorithms with existing methodologies.
Figure 15
Figure 15
F score analysis of HRO-DLBLCC algorithms with existing methodologies.
See this image and copyright information in PMC

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