Studies in differentiating psoriasis from other dermatoses using small data set and transfer learning

Nieniewski, Mariusz; Chmielewski, Leszek J.; Patrzyk, Sebastian; Woźniacka, Anna

doi:10.1186/s13640-023-00607-y

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Their method achieved the sensitivity of 85.33% and a precision of 82.58%. Similar to many other investigations the paper only has focused on Psoriasis 24 .…”

Section: Introductionmentioning

confidence: 93%

Efficient diagnosis of psoriasis and lichen planus cutaneous diseases using deep learning approach

Eskandari,

Sharbatdar

2024

Sci Rep

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The tendency of skin diseases to manifest in a unique and yet similar appearance, absence of enough competent dermatologists, and urgency of diagnosis and classification on time and accurately, makes the need of machine aided diagnosis blatant. This study is conducted with the purpose of broadening the research in skin disease diagnosis with computer by traversing the capabilities of deep Learning algorithms to classify two skin diseases noticeably close in appearance, Psoriasis and Lichen Planus. The resemblance between these two skin diseases is striking, often resulting in their classification within the same category. Despite this, there is a dearth of research focusing specifically on these diseases. A customized 50 layers ResNet-50 architecture of convolutional neural network is used and the results are validated through fivefold cross-validation, threefold cross-validation, and random split. By utilizing advanced data augmentation and class balancing techniques, the diversity of the dataset has increased, and the dataset imbalance has been minimized. ResNet-50 has achieved an accuracy of 89.07%, sensitivity of 86.46%, and specificity of 86.02%. With their promising results, these algorithms make the potential of machine aided diagnosis clear. Deep Learning algorithms could provide assistance to physicians and dermatologists by classification of skin diseases, with similar appearance, in real-time.

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“…Their method achieved the sensitivity of 85.33% and a precision of 82.58%. Similar to many other investigations the paper only has focused on Psoriasis 24 .…”

Section: Introductionmentioning

confidence: 93%

Efficient diagnosis of psoriasis and lichen planus cutaneous diseases using deep learning approach

Eskandari,

Sharbatdar

2024

Sci Rep

View full text Add to dashboard Cite

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A comprehensive study on the application of machine learning in psoriasis diagnosis and treatment: taxonomy, challenges and recommendations

Ghorbian,

Ghobaei-Arani,

Ghorbian

2024

Artif Intell Rev

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CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis

Chakraborty,

Achar,

Nayek

et al. 2024

Sci Rep

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Psoriasis, being a chronic, inflammatory, lifelong skin disorder, has become a major threat to the human population. The precise and effective diagnosis of psoriasis continues to be difficult for clinicians due to its varied nature. In northern India, the prevalence of psoriasis among adult population ranges from 0.44 to 2.8%. Chronic plaque psoriasis accounts for over 90% of cases. This study utilized a dataset of 325 raw images collected from a reputable local hospital using a digital camera under uniform lighting conditions. These images were processed to generate 496 image patches (both diseased and normal), which were then normalized and resized for model training. An automated psoriasis image recognition framework was developed using four state-of-the-art deep transfer learning models: VGG16, VGG19, MobileNetV1, and ResNet-50. The convolutional layers adopted various edge, shape, and color filters to generate the feature map for psoriasis detection. Each pre-trained model was adapted with two dense layers, one dropout layer, and one output layer to classify input images. Among these models, MobileNetV1 achieved the best performance, with 94.84% sensitivity, 89.37% specificity, and 97.24% overall accuracy. Hyper-parameter tuning was performed using grid search to optimize learning rates, batch sizes, and dropout rates. The AdaGrad (Adaptive gradient)) optimizer was chosen for its adaptive learning rate capabilities, facilitating quicker convergence in model performance. Consequently, the methodology’s performance improved to 94.25% sensitivity, 96.42% specificity, and 99.13% overall accuracy. The model’s performance was also compared with non-machine learning-based diagnostic methods, yielding a Dice coefficient of 0.98. However, the model’s effectiveness is dependent upon high-quality input images, as poor image conditions may affect accuracy, and it may not generalize well across diverse demographics or psoriasis variations, highlighting the need for varied training datasets for robustness.

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Studies in differentiating psoriasis from other dermatoses using small data set and transfer learning

Cited by 4 publications

References 23 publications

Efficient diagnosis of psoriasis and lichen planus cutaneous diseases using deep learning approach

Efficient diagnosis of psoriasis and lichen planus cutaneous diseases using deep learning approach

A comprehensive study on the application of machine learning in psoriasis diagnosis and treatment: taxonomy, challenges and recommendations

CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis

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