Leveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox disease

Abstract

The World Health Organization declared a state of emergency in 2022 because of monkeypox. This disease has raised international concern as it has spread beyond Africa, where it is endemic. The global community has shown attention and solidarity in combating this disease as its daily increase becomes evident. Various skin symptoms appear in people infected with this disease, which can spread easily, especially in a polluted environment. It is difficult to diagnose monkeypox in its early stages because of its similarity with the symptoms of other diseases such as chicken pox and measles. Recently, computer-aided classification methods such as deep learning and machine learning within artificial intelligence have been employed to detect various diseases, including COVID-19, tumor cells, and Monkeypox, in a short period and with high accuracy. In this study, we propose the CanDark model, an end-to-end deep-learning model that incorporates cancelable biometrics for diagnosing Monkeypox. CanDark stands for cancelable DarkNet-53, which means that DarkNet-53 CNN is utilized for extracting deep features from Monkeypox skin images. Then a cancelable method is applied to these features to protect patient information. Various cancelable techniques have been evaluated, such as bio-hashing, multilayer perceptron (MLP) hashing, index-of-maximum Gaussian random projection-based hashing (IoM-GRP), and index-of-maximum uniformly random permutation-based hashing (IoM-URP). The proposed approach's performance is evaluated using various assessment issues such as accuracy, specificity, precision, recall, and fscore. Using the IoM-URP, the CanDark model is superior to other state-of-the-art Monkeypox diagnostic techniques. The proposed framework achieved an accuracy of 98.81%, a specificity of 98.73%, a precision of 98.9%, a recall of 97.02%, and f_score of 97.95%.

Keywords: And DarkNet-53; CNN; Cancelable techniques; Monkeypox.

Fig. 1

The proposed CanDark model.

Fig. 2

Sample of images of the classes (Chickenpox, Measles, Monkeypox, and Normal) of the Monkeypox Skin Image Dataset.

Fig. 3

Bio-hashing representation.

Fig. 4

MLP layers and representation in a matrix.

Fig. 5

IoM-GRP algorithm.

Fig. 6

IoM-URP algorithm.

Fig. 7

Graphical representation of the proposed framework using Bio-Hashing cancelable technique.

Fig. 8

Error bar of accuracy, specificity, precision, recall, and f_score of the proposed framework using Bio-Hashing cancelable technique.

Fig. 9

Graphical representation of the proposed framework using MLP Hashing cancelable technique.

Fig. 10

Error bar of accuracy, specificity, precision, recall, and f_score of the proposed framework using MLP Hashing cancelable technique.

Fig. 11

Graphical representation of the proposed framework using IoM-GRP cancelable technique.

Fig. 12

Error bar of accuracy, specificity, precision, recall, and f_score of the proposed framework using IoM-GRP cancelable technique.

Fig. 13

Graphical representation of the proposed framework using IoM-URP cancelable technique.

Fig. 14

Error bar of accuracy, specificity, precision, recall, and f_score of the proposed framework using IoM-URP cancelable technique.

Fig. 15

Normal Q-Q plot of the accuracy of the proposed framework using IoM-URP cancelable technique.

Fig. 16

Detrended normal Q-Q plot of the accuracy of the proposed framework using IoM-URP cancelable technique.

Fig. 17

Normal Q-Q plot of the specificity of the proposed framework using IoM-URP cancelable technique.

Fig. 18

Detrended normal Q-Q plot of the specificity of the proposed framework using IoM-URP cancelable technique.

Fig. 19

Normal Q-Q plot of the precision of the proposed framework using IoM-URP cancelable technique.

Fig. 20

Detrended normal Q-Q plot of the precision of the proposed framework using IoM-URP cancelable technique.

Fig. 21

Normal Q-Q plot of the recall of the proposed framework using IoM-URP cancelable technique.

Fig. 22

Detrended normal Q-Q plot of the recall of the proposed framework using IoM-URP cancelable technique.

Fig. 23

Normal Q-Q plot of the f_score of the proposed framework using IoM-URP cancelable technique.

Fig. 24

Detrended normal Q-Q plot of the f_score of the proposed framework using IoM-URP cancelable technique.

Fig. 25

Graphical comparison between accuracy, specificity, precision, recall, and f_score of the proposed framework under various cancelable techniques.