Lyme Disease Detection Using Progressive Resizing and Self-Supervised Learning Algorithmslyme Disease Detection Using Progressive Resizing and Self-Supervised Learning Algorithms

Jacob, Daryl; Nankar, Om; Gite, Shilpa; Patil, Shruti; Kotecha, Ketan

doi:10.2139/ssrn.4059738

Cited by 2 publications

(2 citation statements)

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“…developed a dataset and detected erythema migrans with 94% accuracy using machine learning. Jacob et al 38 . used self‐supervised learning algorithms for Lyme disease classification with progressive resizing and referred to a self‐created dataset.…”

Section: Related Workmentioning

confidence: 99%

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Lyme rashes disease classification using deep feature fusion technique

Ali,

Anwar,

Nauman

et al. 2023

Skin Research and Technology

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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.

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Section: Related Workmentioning

confidence: 99%

“…Jacob et al. 38 used self‐supervised learning algorithms for Lyme disease classification with progressive resizing and referred to a self‐created dataset. Another study used XResNet‐18 and XResNet34 models for identifying the rash‐causing infection and got an accuracy rate of 84%.…”

Section: Related Workmentioning

confidence: 99%