Community perceptions of suspicious pigmented skin lesions: are they accurate when compared to general practitioners?

2005 ◽  
Vol 29 (3) ◽  
pp. 267-275 ◽  
Author(s):  
Peter D. Baade ◽  
Kevin P. Balanda ◽  
Warren R. Stanton ◽  
John B. Lowe ◽  
Chris B. Del Mar
Keyword(s):  
General Practitioners ◽  
Skin Lesions ◽  
Community Perceptions ◽  
Pigmented Skin Lesions

scholarly journals Deep Neural Frameworks Improve the Accuracy of General Practitioners in the Classification of Pigmented Skin Lesions

Diagnostics ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 969
Author(s):  
Maximiliano Lucius ◽  
Jorge De All ◽  
José Antonio De All ◽  
Martín Belvisi ◽  
Luciana Radizza ◽  
...  
Keyword(s):  
General Practitioners ◽  
Skin Lesions ◽  
Image Resolution ◽  
Aggregated Data ◽  
Skin Imaging ◽  
High Image Resolution ◽  
Random Dataset ◽  
Algorithmic Framework ◽  
Diagnosis Accuracy ◽  
Pigmented Skin Lesions

This study evaluated whether deep learning frameworks trained in large datasets can help non-dermatologist physicians improve their accuracy in categorizing the seven most common pigmented skin lesions. Open-source skin images were downloaded from the International Skin Imaging Collaboration (ISIC) archive. Different deep neural networks (DNNs) (n = 8) were trained based on a random dataset constituted of 8015 images. A test set of 2003 images was used to assess the classifiers’ performance at low (300 × 224 RGB) and high (600 × 450 RGB) image resolution and aggregated data (age, sex and lesion localization). We also organized two different contests to compare the DNN performance to that of general practitioners by means of unassisted image observation. Both at low and high image resolution, the DNN framework differentiated dermatological images with appreciable performance. In all cases, the accuracy was improved when adding clinical data to the framework. Finally, the least accurate DNN outperformed general practitioners. The physician’s accuracy was statistically improved when allowed to use the output of this algorithmic framework as guidance. DNNs are proven to be high performers as skin lesion classifiers and can improve general practitioner diagnosis accuracy in a routine clinical scenario.

scholarly journals Deep neural frameworks improve the accuracy of general practitioners in the classification of pigmented skin lesions

2020 ◽  
Author(s):  
Maximiliano Lucius ◽  
Jorge De All ◽  
José Antonio De All ◽  
Martín Belvisi ◽  
Luciana Radizza ◽  
...  
Keyword(s):  
High Resolution ◽  
General Practitioners ◽  
Clinical Data ◽  
Skin Lesions ◽  
Classification Performance ◽  
Image Resolution ◽  
Deep Convolutional Neural Networks ◽  
Random Dataset ◽  
Pigmented Skin Lesions

AbstractArtificial intelligence can be a key tool in the context of assisting in the diagnosis of dermatological conditions, particularly when performed by general practitioners with limited or no access to high resolution optical equipment. This study evaluates the performance of deep convolutional neural networks (DNNs) in the classification of seven pigmented skin lesions. Additionally, it assesses the improvement ratio in the classification performance when utilized by general practitioners. Open-source skin images were downloaded from the ISIC archive. Different DNNs (n=8) were trained based on a random dataset constituted by 8,015 images. A test set of 2,003 images has been used to assess the classifiers performance at low (300 × 224 RGB) and high (600 × 450 RGB) image resolution and aggregated clinical data (age, sex and lesion localization). We have also organized two different contests to compare the DNNs performance to that of general practitioners by means of unassisted image observation. Both at low and high image resolution, the DNNs framework being trained differentiated dermatological images with appreciable performance. In all cases, accuracy has been improved when adding clinical data to the framework. Finally, the lowest accurate DNN outperformed general practitioners. Physician’s accuracy was statistically improved when allowed to use the output of this algorithmic framework as guidance. DNNS are proven to be high performers as skin lesion classifiers. The aim is to include these AI tools in the context of general practitioners whilst improving their diagnosis accuracy in a routine clinical scenario when or where the use of high-resolution equipment is not accessible.

scholarly journals Robustness of convolutional neural networks in recognition of pigmented skin lesions

2021 ◽  
Vol 145 ◽  
pp. 81-91
Author(s):  
Roman C. Maron ◽  
Sarah Haggenmüller ◽  
Christof von Kalle ◽  
Jochen S. Utikal ◽  
Friedegund Meier ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Lesions ◽  
Pigmented Skin Lesions

Symmetrical acral keratoderma: A waxing and waning scaly pigmented skin lesions on the acral extremities

2020 ◽  
Author(s):  
Toshifumi Nomura ◽  
Masae Takeda ◽  
Jin Teng Peh ◽  
Akihiro Orita ◽  
Emi Inamura ◽  
...  
Keyword(s):  
Skin Lesions ◽  
Pigmented Skin Lesions

scholarly journals From colour to tissue histology: Physics-based interpretation of images of pigmented skin lesions

2003 ◽  
Vol 7 (4) ◽  
pp. 489-502 ◽  
Author(s):  
Ela Claridge ◽  
Symon Cotton ◽  
Per Hall ◽  
Marc Moncrieff
Keyword(s):  
Skin Lesions ◽  
Pigmented Skin Lesions ◽  
Tissue Histology

scholarly journals Q-Switched Neodymium-Doped Yttrium Aluminum Garnet Laser Therapy for Pigmented Skin Lesions: Efficacy and Safety

2013 ◽  
Vol 10 (2) ◽  
pp. 46-50 ◽  
Author(s):  
D Karn ◽  
S KC ◽  
A Amatya ◽  
EA Razouria ◽  
M Timalsina ◽  
...  
Keyword(s):  
Recurrence Rate ◽  
Safety Profile ◽  
Yttrium Aluminum Garnet ◽  
Skin Lesions ◽  
University Hospital ◽  
Efficacy And Safety ◽  
Yag Laser ◽  
Pigmentary Disorders ◽  
Yttrium Aluminum ◽  
Pigmented Skin Lesions

Background Nepalese population with Fitzpatrick skin types III-V has high prevalence of pigmentary disorders and it is a growing cosmetic concern. Q-Switched Neodymium- Doped Yttrium Aluminum Garnet (QS Nd-YAG) laser is an efficacious tool in the treatment of pigment disorders. Objective To highlight the efficacy and safety profile of various pigment disorders. Methods A prospective study done in Dhulikhel Hospital, Kathmandu University Hospital from January 2009 to January 2011. Patients undergoing laser for pigmented skin lesions were followed for response and safety profile. We included total 270 patients in the study with various disorders especially nevus, tattoos and melasma. Settings were repeated at 3-4 weeks interval and response was evaluated on clinical basis. Efficacy was then evaluated according to various parameters. Results For nevus, total 840 treatment sessions had been performed with an average of 6.88 sessions (range 3-11). Nd: YAG laser was very efficacious in removal of blue and black colored tattoos with an average of 7.9 and 9.5 sessions respectively. However, red mixed with blue and or green tattoos were relatively resistant to treatment and required average 10.33 treatment sessions. Melasma and freckles both responded to the therapy but recurrence rate was high. Conclusion Our results indicate that QS Nd: YAG laser is an effective modality for pigment disorders among Nepalese population. Nevus and melasma respond well but recurrence rate of melasma is high. Blue tattoos respond well while mixed colored tattoos are quite resistant to Nd: YAG laser alone. Transient pain and temporary hyperpigmentation are common side effects. Kathmandu University Medical Journal | Vol.10 | No. 2 | Issue 38 | Apr – June 2012 | Page 46-50 DOI: http://dx.doi.org/10.3126/kumj.v10i2.7343

WITHDRAWN: Computational methods for the image segmentation of pigmented skin lesions: A Review

2016 ◽  
Author(s):  
Roberta B. Oliveira ◽  
Mercedes E. Filho ◽  
Zhen Ma ◽  
João P. Papa ◽  
Aledir S. Pereira ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Computational Methods ◽  
Skin Lesions ◽  
Pigmented Skin Lesions
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