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. 2023 Dec 1;11(12):3200.
doi: 10.3390/biomedicines11123200.

Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images

Eunmok Yang  1 , K Shankar  2   3 , Sachin Kumar  4 , Changho Seo  5 , Inkyu Moon  6
Affiliations

Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images

Eunmok Yang et al. Biomedicines. .

Abstract

The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics.

Keywords: cancer diagnosis; deep learning; equilibrium optimizer; magnetic resonance imaging; prostate cancer.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The overall working flow of the EOADL-PCDC technique.
Figure 2
Figure 2
BiLSTM structure.
Figure 3
Figure 3
Confusion matrices of (a,b) 80% of TRAP/20% of TESP and (c,d) 70% of TRAP/30% of TESP.
Figure 4
Figure 4
Average of the EOADL-PCDC system on 80:20 of TRAP/TESP.
Figure 5
Figure 5
Accuy curve of the EOADL-PCDC algorithm on 80:20 of TRAP/TESP.
Figure 6
Figure 6
Loss curve of the EOADL-PCDC system on 80:20 of TRAP/TESP.
Figure 7
Figure 7
PR analysis of EOADL-PCDC technique on 80:20 of TRAP/TESP.
Figure 8
Figure 8
ROC analysis of the EOADL-PCDC technique on 80:20 of TRAP/TESP.
Figure 9
Figure 9
Average of the EOADL-PCDC method on 70:30 of TRAP/TESP.
Figure 10
Figure 10
Accuy curve of the EOADL-PCDC technique on 70:30 of TRAP/TESP.
Figure 11
Figure 11
Loss curve of the EOADL-PCDC technique on 70:30 of TRAP/TESP.
Figure 12
Figure 12
PR curve of the EOADL-PCDC algorithm on 70:30 of TRAP/TESP.
Figure 13
Figure 13
ROC curve of the EOADL-PCDC model on 70:30 of TRAP/TESP.
Figure 14
Figure 14
Comparative outcome of the EOADL-PCDC algorithm with recent systems.
See this image and copyright information in PMC

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