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.

Deep hybrid model for Mpox disease diagnosis from skin lesion images

“…These studies illustrate the promising potential of DL and TL methods for medical image classification, including detecting infectious diseases like MPox 3 and Lyme disease 27 . However, challenges related to model interpretability, generalization on different datasets, and computational efficiency remain, requiring further research.…”

“…Furthermore, novel approaches integrating AI‐based optimization techniques have been proposed for MPox diagnosis. These approaches involve fine‐tuning customized CNN layers using AI‐Biruni Earth Radius (BER) 3 optimization‐based stochastic fractal search (BERSFS) 22 and employing optimization algorithms like sine cosine (SC) and particle swarm (PS) for feature selection with multilayer perceptron (MLP) classifiers. Additionally, several studies utilized various CNN architectures 13 to differentiate between MPox and non‐MPox cases using both MSID and MSLD datasets.…”

“…While monkeys and rodents primarily transmit the virus, human‐to‐human spread is also prevalent. The first 3 human case of MPox was identified in the Democratic Republic of the Congo in 1970 during efforts to eradicate smallpox. Historically, MPox has been more commonly observed in central and western Africa, particularly among individuals living near tropical rainforests.…”

MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning

“…A further dataset was introduced called Monkeypox Skin Image Dataset (MSID) 47 and was utilized in a number of studies. For example, the study 3 proposed a new approach for fine-tuning customized CNN layers for identifying monkeypox disease from photos utilizing AI-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS). Likewise, the research article 36 employed the BER optimization approach along with the sine cosine (SC) procedure and the particle swarm (PS) optimization process to select features extracted from four CNNs separately using TL and optimized parameters of a multilayer perceptron (MLP) classifier.…”

“…Nevertheless, it has recently been observed in urban regions out beyond Africa. 3 Because of monkeypox's quick propagation in 19 countries beyond the outbreak zones, 4 the recent multi-region pandemic of the monkeypox disease poses a serious worldwide health concern. 5 The globe could not afford a further disease outbreak, while the effects of the previous one are still being felt.…”