AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata
Received: 15 March 2023 | Revised: 7 April 2023 | Accepted: 17 April 2023 | Online: 2 June 2023
Corresponding author: Chinnaiyan Saraswathi
Abstract
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.
Keywords:
GAN, artificial intelligence, Alopecia areata, deep learning, data augmentationDownloads
References
K. York, N. Meah, B. Bhoyrul, and R. Sinclair, "A review of the treatment of male pattern hair loss," Expert Opinion on Pharmacotherapy, vol. 21, no. 5, pp. 603–612, Mar. 2020. DOI: https://doi.org/10.1080/14656566.2020.1721463
A. Ahmad, F. Khatoon, B. Khan, M. Mohsin, Lucknow, and A. Aligarh, "A Critical Review of Daus-Sadaf (Psoriasis): Unani & Modern Perspectives," International Journal of Creative Research Thoughts, vol. 8, no. 7, pp. 4570–4582, Jul. 2020.
D. I. Conde Hurtado, J. I. Vergara Rueda, J. L. Bermudez Florez, S. C. Cadena Infante, and A. J. Rodriguez Morales, "Potential Dermatological Conditions Resulting from a Prolonged Stay at Home during the COVID-19 Pandemic: A Review," Acta dermatovenerologica Croatica: ADC, vol. 29, no. 3, pp. 135–147, Dec. 2021.
M. J. Adelman, L. M. Bedford, and G. A. Potts, "Clinical efficacy of popular oral hair growth supplement ingredients," International Journal of Dermatology, vol. 60, no. 10, pp. 1199–1210, Oct. 2021. DOI: https://doi.org/10.1111/ijd.15344
A. Egger, M. Tomic-Canic, and A. Tosti, "Advances in Stem Cell-Based Therapy for Hair Loss," CellR4-- repair, replacement, regeneration, & reprogramming, vol. 8, Sep. 2020, Art. no. e2894.
F. Diotallevi, O. Simonetti, G. Rizzetto, E. Molinelli, G. Radi, and A. Offidani, "Biological Treatments for Pediatric Psoriasis: State of the Art and Future Perspectives," International Journal of Molecular Sciences, vol. 23, no. 19, Jan. 2022, Art. no. 11128. DOI: https://doi.org/10.3390/ijms231911128
L. C. Coates et al., "Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA): updated treatment recommendations for psoriatic arthritis 2021," Nature Reviews Rheumatology, vol. 18, no. 8, pp. 465–479, Aug. 2022.
M. Sreenatha and P. B. Mallikarjuna, "A Fault Diagnosis Technique for Wind Turbine Gearbox: An Approach using Optimized BLSTM Neural Network with Undercomplete Autoencoder," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 10170–10174, Feb. 2023. DOI: https://doi.org/10.48084/etasr.5595
H. Reffad, A. Alti, and A. Almuhirat, "A Dynamic Adaptive Bio-Inspired Multi-Agent System for Healthcare Task Deployment," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 10192–10198, Feb. 2023. DOI: https://doi.org/10.48084/etasr.5570
V. C. Ho, T. H. Nguyen, T. Q. Nguyen, and D. D. Nguyen, "Application of Neural Networks for the Estimation of the Shear Strength of Circular RC Columns," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9409–9413, Dec. 2022. DOI: https://doi.org/10.48084/etasr.5245
W.-J. Chang, L.-B. Chen, M.-C. Chen, Y.-C. Chiu, and J.-Y. Lin, "ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health," IEEE Access, vol. 8, pp. 134826–134837, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3010847
F. Zucchelli, N. Sharratt, K. Montgomery, and J. Chambers, "Men’s experiences of alopecia areata: A qualitative study," Health Psychology Open, vol. 9, no. 2, Jul. 2022, Art. no. 205510292211215. DOI: https://doi.org/10.1177/20551029221121524
A. Alessandrini, F. Bruni, B. m. Piraccini, and M. Starace, "Common causes of hair loss – clinical manifestations, trichoscopy and therapy," Journal of the European Academy of Dermatology and Venereology, vol. 35, no. 3, pp. 629–640, 2021. DOI: https://doi.org/10.1111/jdv.17079
A. Elder, C. Ring, K. Heitmiller, Z. Gabriel, and N. Saedi, "The role of artificial intelligence in cosmetic dermatology—Current, upcoming, and future trends," Journal of Cosmetic Dermatology, vol. 20, no. 1, pp. 48–52, 2021. DOI: https://doi.org/10.1111/jocd.13797
C. Saraswathi, B. Pushpa, "Computer Imaging of Alopecia Areata and Scalp Detection: A Survey," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 347-358, 2022. DOI: https://doi.org/10.14445/22315381/IJETT-V70I8P236
C. Saraswathi and B. Pushpa, "Machine Learning Algorithm for Classification of Alopecia Areata from Human Scalp Hair Images," in Computational Vision and Bio-Inspired Computing, Singapore, 2023, pp. 269–288. DOI: https://doi.org/10.1007/978-981-19-9819-5_21
R. Takahashi, T. Matsubara, and K. Uehara, "Data Augmentation Using Random Image Cropping and Patching for Deep CNNs," IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 9, pp. 2917–2931, Sep. 2020. DOI: https://doi.org/10.1109/TCSVT.2019.2935128
Z. Qin, Z. Liu, P. Zhu, and Y. Xue, "A GAN-based image synthesis method for skin lesion classification," Computer Methods and Programs in Biomedicine, vol. 195, Oct. 2020, Art. no. 105568. DOI: https://doi.org/10.1016/j.cmpb.2020.105568
H. Xu et al., "An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data," IEEE Access, vol. 8, pp. 187455–187469, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3031059
I. S. A. Abdelhalim, M. F. Mohamed, and Y. B. Mahdy, "Data augmentation for skin lesion using self-attention based progressive generative adversarial network," Expert Systems with Applications, vol. 165, p. 113922, Mar. 2021. DOI: https://doi.org/10.1016/j.eswa.2020.113922
K. Guo, T. Luo, M. Z. A. Bhuiyan, S. Ren, J. Zhang, and D. Zhou, "Zero shot augmentation learning in internet of biometric things for health signal processing," Pattern Recognition Letters, vol. 146, pp. 142–149, Jun. 2021. DOI: https://doi.org/10.1016/j.patrec.2021.03.012
S. Motamed, P. Rogalla, and F. Khalvati, "Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images," Informatics in Medicine Unlocked, vol. 27, Jan. 2021, Art. no. 100779. DOI: https://doi.org/10.1016/j.imu.2021.100779
C. Chen et al., "Enhancing MR image segmentation with realistic adversarial data augmentation," Medical Image Analysis, vol. 82, Nov. 2022, Art. no. 102597. DOI: https://doi.org/10.1016/j.media.2022.102597
S. Nesteruk et al., "XtremeAugment: Getting More From Your Data Through Combination of Image Collection and Image Augmentation," IEEE Access, vol. 10, pp. 24010–24028, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3154709
"Figaro 1K," Figaro 1K | share Your Project. http://projects.i-ctm.eu/it/progetto/figaro-1k.
"DermNet skin disease atlas," DermNet | Dermatology Resource. https://dermnet.com.
Downloads
How to Cite
License
Copyright (c) 2023 Chinnaiyan Saraswathi, Balasubramanian Pushpa
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
