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 Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis

2021 ◽  
Vol 3 (1) ◽  
pp. 34-42
Author(s):  
Vivekanadam B
Keyword(s):  
Artificial Intelligence ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Melanoma Diagnosis ◽  
Single Lens ◽  
Diagnosis Accuracy ◽  
Artificial Intelligence Algorithm ◽  
Usb Camera ◽  
Pigmented Skin Lesions ◽  
Diagnostic Trial

Of all suspicious pigmented skin lesions considered for analysis, a large portion is often benign. The pressure of pathology services and secondary care must be reduced throughout the patient trials using modern techniques for improving the melanoma diagnosis accuracy. Dermoscopic images obtained from digital single-lens reflex (DSLR) cameras, smartphones and a lightweight USB camera are compared using artificial intelligence (AI) algorithm for determining the accuracy of melanoma identification. Datasets are obtained from thousand test samples undergoing plastic surgery. The diagnostic trial is masked, single arm and multicentered. The controlled and suspicious skin lesions as well as the suspicious pigmented skin lesion are captured on the aforementioned cameras while scheduling for biopsy. The possibility of melanoma is assessed using deep learning (DL) techniques on the pigmented skin lesions seen in the dermascopic images for identifying melanoma. For this purpose, we train a deterministic AI algorithm based on malignancy recognition by deep ensemble and inputs from clinicians. The histopathology diagnosis is used as a standard criterion for determining the specialist assessment, algorithmic specificity, sensitivity and the area under the receiver operating characteristic curve (AUROC).

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

Methodology for TEM Analysis of Barrier Profiles

2002 ◽  
Author(s):  
Tan-Chen Lee ◽  
Jui-Yen Huang ◽  
Li-Chien Chen ◽  
Ruey-Lian Hwang ◽  
David Su
Keyword(s):  
Sample Preparation ◽  
Profile Analysis ◽  
Image Interpretation ◽  
Sample Thickness ◽  
Image Resolution ◽  
Specimen Preparation ◽  
Tem Analysis ◽  
Preparation Methods ◽  
High Image Resolution ◽  
Common Technique

Abstract Device shrinkage has resulted in thinner barriers and smaller vias. Transmission Electron Microscopy (TEM) has become a common technique for barrier profile analysis because of its high image resolution. TEM sample preparation and image interpretation becomes difficult when the size of the small cylindrical via is close to the TEM sample thickness. Effects of different sample thickness and specimen preparation methods, therefore, have been investigated. An automatic FIB program has been shown to be useful in via sample preparation. Techniques for imaging a TEM specimen will be discussed in the paper. Conventional TEM bright field (BF) image is adequate to examine the barrieronly via; however, other techniques are more suitable for a Cu filled via.

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 Identification of Skin Lesions by Using Single-Step Multiframe Detector

2021 ◽  
Vol 10 (1) ◽  
pp. 144
Author(s):  
Yu-Ping Hsiao ◽  
Chih-Wei Chiu ◽  
Chih-Wei Lu ◽  
Hong Thai Nguyen ◽  
Yu Sheng Tseng ◽  
...  
Keyword(s):  
Ground Truth ◽  
Skin Lesions ◽  
Single Step ◽  
Single Shot ◽  
Accuracy Rate ◽  
Tissue Slices ◽  
Image Detection ◽  
Sensitivity Rate ◽  
Diagnosis Accuracy ◽  
Artificial Intelligence Algorithm

An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor.

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
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