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. 2023 Nov;29(11):e13519.
doi: 10.1111/srt.13519.

Lyme rashes disease classification using deep feature fusion technique

Ghulam Ali  1 , Muhammad Anwar  2 , Muhammad Nauman  1 , Muhammad Faheem  3 , Javed Rashid  4   5
Affiliations

Lyme rashes disease classification using deep feature fusion technique

Ghulam Ali et al. Skin Res Technol. 2023 Nov.

Abstract

Automatic classification of Lyme disease rashes on the skin helps clinicians and dermatologists' probe and investigate Lyme skin rashes effectively. This paper proposes a new in-depth features fusion system to classify Lyme disease rashes. The proposed method consists of two main steps. First, three different deep learning models, Densenet201, InceptionV3, and Exception, were trained independently to extract the deep features from the erythema migrans (EM) images. Second, a deep feature fusion mechanism (meta classifier) is developed to integrate the deep features before the final classification output layer. The meta classifier is a basic deep convolutional neural network trained on original images and features extracted from base level three deep learning models. In the feature fusion mechanism, the last three layers of base models are dropped out and connected to the meta classifier. The proposed deep feature fusion method significantly improved the classification process, where the classification accuracy was 98.97%, which is particularly impressive than the other state-of-the-art models.

Keywords: Lyme disease; artificial intelligence; convolutional neural network classification; erythema migrans; fusion technique.

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Conflict of interest statement

The authors have no conflict of interests.

Figures

FIGURE 1
FIGURE 1
(A) Lyme disease positive (B) Lyme disease negative.
FIGURE 2
FIGURE 2
The flowchart of the proposed model deep feature fusion.
FIGURE 3
FIGURE 3
InceptionV3 architecture diagram.
FIGURE 4
FIGURE 4
DenseNet201 architecture diagram.
FIGURE 5
FIGURE 5
Xception architecture diagram.
FIGURE 6
FIGURE 6
Accuracy, and loss graph of the proposed model.
FIGURE 7
FIGURE 7
The suggested method's classification accuracy, precision, recall, and f1 score.
FIGURE 8
FIGURE 8
The proposed model confusion matrix on test set.
See this image and copyright information in PMC

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