Pictures of skin lesions
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Summary
10 papers analyzed
These studies suggest that various deep learning algorithms, including AlexNet, ResNet-152, and modified U-Net, as well as decision trees and random forest algorithms, are effective in identifying and classifying skin lesions, although challenges in generalization and representation remain.
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Automated Identification and Analysis of Skin Lesions Using Deep Learning and Smartphone Technology
Deep Learning for Skin Lesion Identification
Automated Identification of Cutaneous Leishmaniasis Lesions
Deep learning (DL) algorithms have shown significant promise in the automated identification of skin lesions, particularly for diseases like cutaneous leishmaniasis (CL). A study evaluated the performance of AlexNet, a DL algorithm, in identifying CL lesions from a dataset of 2,458 images. The algorithm achieved an impressive average accuracy of 95.04%, demonstrating its potential to assist clinicians in diagnosing CL by differentiating it from 26 other dermatoses.
Skin Lesion Classification Using Convolutional Neural Networks
Another study focused on the classification of various skin lesions, including malignant melanoma and basal cell carcinoma, using a ResNet-152 architecture. This model was trained on 3,797 images and tested on 956 images, achieving an area under the curve (AUC) of 0.96 for melanoma and 0.91 for basal cell carcinoma. This high level of accuracy underscores the effectiveness of convolutional neural networks (CNNs) in skin lesion classification.
Modified U-NET for Skin Lesion Segmentation
Accurate segmentation of skin lesions is crucial for effective diagnosis and treatment. A modified U-Net architecture was proposed to improve the segmentation of dermoscopic images. By adjusting the feature map dimensions and incorporating more kernels, the model achieved high accuracy in segmenting skin lesions, particularly when tested with augmented images from the PH2 dataset.
Smartphone Technology in Dermatology
Visualization of Skin Lesions on Smartphones
Smartphones are becoming an increasingly valuable tool in dermatology. A cross-sectional study assessed the utility of patients presenting pictures of their skin lesions during dermatology visits. The study found that these photographs provided relevant diagnostic information, highlighting the potential of smartphones to aid in the early detection and diagnosis of skin conditions.
Online Depiction of Urticaria
Despite the potential benefits, the quality and accuracy of online images of skin lesions can vary. A study evaluating online images of urticaria found that 18% of the images did not depict urticarial lesions, and 19.6% were ambiguous. This underlines the need for improved accuracy and representativeness in online medical images to prevent misdiagnosis and suboptimal treatment.
Advanced Techniques in Skin Lesion Analysis
Accurate Segmentation and Registration of Skin Lesion Images
Accurate segmentation and registration of skin lesion images are critical for evaluating lesion changes over time. A novel segmentation algorithm based on superpixels techniques achieved the best results for the ISIC 2017 challenge dataset. Additionally, an advanced image registration approach was proposed to assess lesion evolution accurately, paving the way for automatic systems to evaluate skin lesion changes.
Skin Lesion Analysis Towards Melanoma Detection
Early detection of melanoma significantly increases survival rates. A deep learning framework consisting of fully convolutional residual networks (FCRN) was proposed to address lesion segmentation, dermoscopic feature extraction, and lesion classification. The framework achieved promising accuracies on the ISIC 2017 dataset, demonstrating its potential in improving melanoma detection.
Conclusion
The integration of deep learning algorithms and smartphone technology is revolutionizing the field of dermatology. From the automated identification of cutaneous leishmaniasis lesions to the accurate segmentation and classification of various skin conditions, these advancements are enhancing diagnostic accuracy and efficiency. However, the quality and representativeness of online medical images remain a concern, necessitating ongoing efforts to improve these resources. As technology continues to evolve, its application in dermatology promises to offer even greater benefits in the diagnosis and treatment of skin lesions.
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