Machine Learning Algorithm for Classification of Alopecia Areata from Human Scalp Hair Images

Saraswathi, C.; Pushpa, B.

doi:10.1007/978-981-19-9819-5_21

Cited by 1 publication

(2 citation statements)

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“…Of the images collected, 70% was used for training and the remaining 30% was used for testing. The considered existing models were the AB-MTEDeep [16], RICAP-CNN [17], EF-GAN-VGG16 [19], and IAGAN [22], which were applied to the Figaro1k and Dermnet databases for the AA classification, using the same proportions for the training and testing of the models. Figure 3 shows the generator and discriminator loss curves in the AA-GAN model, while Figure 4 MTEDeep was 17.6% higher than RICAP-CNN's, 14% higher than the EF-GAN-VGG16's, 8.6% higher than the IAGAN's, and 1.9% higher than that of the AB-MTEDeep model.…”

Section: Resultsmentioning

confidence: 99%

“…But, if the network depth is increased, degradation problem occurrs causing overfitting. Therefore, the Attention-based Balanced Multi-Tasking Ensembling Deep (AB-MTEDeep) model was developed, which adopted cross-residual learning in the LSTM with FRCNN to improve efficiency [16]. However, the robustness and generalization ability of the deep learning models largely depends on the amount of data available in the training phase.…”

Section: Introductionmentioning

confidence: 99%

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AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata

Saraswathi

Pushpa

2023

Eng. Technol. Appl. Sci. Res.

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Artificial Intelligence (AI) is widely used in dermatology to analyze trichoscopy imaging and assess Alopecia Areata (AA) and scalp hair problems. From this viewpoint, the Attention-based Balanced Multi-Tasking Ensembling Deep (AB-MTEDeep) network was developed, which combined the Faster Residual Convolutional Neural Network (FRCNN) and Long Short-Term Memory (LSTM) network with cross residual learning to classify scalp images into different AA classes. This article presents a new data augmentation model called AA-Generative Adversarial Network (AA-GAN) to produce a huge number of images from a set of input images. The structure of AA-GAN and its loss functions are comparable to those of standard GAN, which encompasses a generator and a discriminator network. To generate high-quality AA structure-based images, the generator was trained to extract the 2D orientation and confidence maps along with the bust depth map from real hair and scalp images. The discriminator was also used to separate real from generated images, which were provided as feedback to the generator to create synthetic images that are extremely close to the real input images. The created images were used to train the AB-MTEDeep model for AA classification. Finally, the experimental results exhibited that the AA-GAN-AB-MTEDeep achieved 96.94% accuracy.

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