An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

Abstract

Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework's efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.

Keywords: Al-Biruni Earth radius; deep learning; meta-heuristic; monkeypox infection; optimization; skin disease.

Figure 1

Samples from the Monkeypox Skin Images Dataset (MSID) [63]: (a) monkeypox cases, (b) chickenpox cases, (c) measles cases, and (d) normal cases.

Figure 2

The homoscedasticity graphs, heat maps, residual plots, and QQ plots of the BERSFS and other basic models.

Figure 3

The homoscedasticity graphs, heat maps, residual plots, and QQ plots of the BERSFS-CNN and other deep learning models.

Figure 4

The homoscedasticity graphs, heat maps, residual plots, and QQ plots of the BERSFS-CNN and other optimization-based models.

Figure 5

The box plot of the accuracy of the proposed BERSFS-CNN and comparison approaches.

Figure 6

The accuracy histogram for the algorithms presented and compared with number of values in the bin center range 0.938–0.968.

Figure 7

ROC curve of the proposed BERSFS-CNN algorithm versus the BER-CNN algorithm.

Figure 8

ROC curve of the proposed BERSFS-CNN algorithm versus the WOA-CNN algorithm.