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. 2023 May:83:104635.
doi: 10.1016/j.bspc.2023.104635. Epub 2023 Jan 31.

ExpACVO-Hybrid Deep learning: Exponential Anti Corona Virus Optimization enabled Hybrid Deep learning for tongue image segmentation towards diabetes mellitus detection

Jimsha K Mathew  1 , S Sathyalakshmi  1
Affiliations

ExpACVO-Hybrid Deep learning: Exponential Anti Corona Virus Optimization enabled Hybrid Deep learning for tongue image segmentation towards diabetes mellitus detection

Jimsha K Mathew et al. Biomed Signal Process Control. 2023 May.

Abstract

A metabolic disease known as diabetes mellitus (DM) is primarily brought on by an increase in blood sugar levels. On the other hand, DM and the complications it causes, such as diabetic Retinopathy (DR), will quickly emerge as one of the major health challenges of the twenty-first century. This indicates a huge economic burden on health-related authorities and governments. The detection of DM in the earlier stage can lead to early diagnosis and a considerable drop in mortality. Therefore, in order to detect DM at an early stage, an efficient detection system having the ability to detect DM is required. An effective classification method, named Exponential Anti Corona Virus Optimization (ExpACVO) is devised in this research work for Diabetes Mellitus (DM) detection using tongue images. Here, the UNet-Conditional Random Field-Recurrent Neural Network (UNet-CRF-RNN) is used to segment the images, and the proposed ExpACVO algorithm is used to train the UNet-CRF-RNN. Deep Q Network (DQN) classifier is used for DM detection, and the proposed ExpACVO is used for DQN training. The proposed ExpACVO algorithm is a newly created formula that combines Anti Corona Virus Optimization(ACVO) with Exponential Weighted Moving Average (EWMA). With maximum testing accuracy, sensitivity, and specificity values of 0.932, 0.950, and 0.914, respectively, the developed technique thus achieved improved performance.

Keywords: Anti Corona Virus Optimization; Diabetes Mellitus; Diabetes Mellitus detection; Exponential Weighted Moving Average; Image segmentation.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Normal and affected tongue image of diabetis mellitus patient.
Fig. 2
Fig. 2
Schematic flow of the developed ExpACVO-enabled Hybrid deep learning for tongue image segmentation towards DM detection.
Fig. 3
Fig. 3
Structure of U Net-CRF-RNN.
Fig. 4
Fig. 4
Architecture of DQN.
Fig. 5
Fig. 5
Outcomes of the developed method i) input image, ii) pre-processed image, iii) segmented image, iv) horizontally flipped image, v) vertically flipped image, vi) rotated image.
Fig. 6
Fig. 6
Performance analysis of the developed technique using training data a) testing accuracy, b) sensitivity, and c) specificity.
Fig. 7
Fig. 7
Training data analysis of the developed segmentation method using based on a) segmentation accuracy.
Fig. 8
Fig. 8
K-Fold analysis of the developed segmentation method using based on a) segmentation accuracy.
Fig. 9
Fig. 9
Comparative assessment of the designed ExpACVO-based DQN technique using training data i) testing accuracy, ii) sensitivity, iii) specificity.
Fig. 10
Fig. 10
Comparative analysis of the developed technique using K-fold i) testing accuracy, ii) sensitivity, and iii) specificity.
Fig. 11
Fig. 11
Algorithmic assessment of the designed ExpACVO-enabled DQN method considering a) Testing accuracy, b) Sensitivity and c) Specificity.
See this image and copyright information in PMC

References

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