Monkeypox diagnosis based on probabilistic K-nearest neighbors (PKNN) algorithm.

Although it is not a new illness and has been around since the previous century, monkeypox later resurgence is fraught with difficulties. This study presents a novel approach of diagnosing monkeypox using artificial intelligence, which is called Effective Monkeypox Diagnosis Strategy (EMDS). The proposed EMDS is established through two sequential stages, namely; (i) Pre-Processing Phase (PP) and (ii) Monkeypox Diagnosing phase (MDP). During PP the input image dataset is prepared through three processes, which are; feature extraction, feature selection, and anomaly rejection, while the actual diagnosis performed in the MDP. Features are extracted from input images using GoogleNet as an effective pre-trained deep learning model, while Leopard Seal Optimization (LSO) is employed to select the most instructive features. The major contribution of this paper is focused in two issues, which are; (i) introducing a new methodology for rejecting anomalies from the input image dataset based on interquartile range (IQR), and (ii) proposing a new instance of K-Nearest Neighbor classifier for monkeypox diagnosis, which is called; Probabilistic K-Nearest Neighbors (PKNN) Algorithm. The proposed PKNN combines evidence from distance based traditional KNN as well as the Naïve probabilistic theorem used by Naïve Bayes (NB) algorithm in an integrated way. Numerous experiments have been conducted considering the proposed EMDS as well as recent competitive strategies on two public monkeypox datasets, which are; the Monkeypox Skin Image and Lesion Datasets (MSID and MSLD, respectively). Initially, the performance of the basic contributions of the proposed EMDS, which are the outlier rejection methodology (ORM) and PKNN, are evaluated individually. Then, EMSD as a whole is evaluated. Moreover, an ablation study has also been conducted to evaluate the effect of ORM and PKNN on the performance of EMSD. Based on the experimental results, it is shown that EMDS outperforms recent monkeypox identification strategies as it achieves 99 % diagnosis accuracy. Moreover, it indicates the maximum precision and recall with the minimum diagnosis time.