An expert diagnostic program for dermatology

Vanker, Anthony D.; Stoecker, W. V.

doi:10.1016/s0010-4809(84)80015-4

Cited by 20 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One study listed specific clinical features characteristic of common benign and malignant tumors using the criteria table technique (60). With this method, characteristics of tumors were included in tables of major and minor criteria typically displayed by members of the diagnostic class.…”

Section: Explicit Expert Systems: Rule-based and Categoricalmentioning

confidence: 99%

Nondermatoscopic digital imaging of pigmented lesions

Stoecker

Moss

Erçal

et al. 1995

Skin Research and Technology

View full text Add to dashboard Cite

Digital imaging of pigmented lesions to this date include acquiring and storing of images, quantification of clinical features including asymmetry, and teledermatology with transfer of images. Predicted uses include malignancy evaluation, delineation of depth of invasion and the development of large collections of pigment lesions observations. The field is rapidly expanding. As of 1994, it is unknown what role digital imaging will ultimately play in clinical dermatology.

show abstract

Section: Explicit Expert Systems: Rule-based and Categoricalmentioning

confidence: 99%

Nondermatoscopic digital imaging of pigmented lesions

Stoecker

Moss

Erçal

et al. 1995

Skin Research and Technology

View full text Add to dashboard Cite

show abstract

“…The availability of larger, more diverse, and better-annotated datasets is one of the main driving factors for the advances in skin image analysis in the past decade (Marchetti et al, 2018;Celebi et al, 2019). Works in skin image analysis date back to the 1980s (Vanker and Van Stoecker, 1984;Dhawan et al, 1984), but until the mid-2000s, they used small, private datasets, containing few hundred images.…”

Section: Datasetsmentioning

confidence: 99%

A Survey on Deep Learning for Skin Lesion Segmentation

Mirikharaji¹,

Barata²,

Kumar³

et al. 2022

Preprint

View full text Add to dashboard Cite

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 134 research papers that deal with deep learning based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. We summarize all examined works in a comprehensive table to facilitate comparisons.

show abstract

“…The clinical feature semitranslucency, a semi‐liquid or jelly‐like appearance that is commonly visible in basal cell carcinomas (BCCs) with the unaided eye, is useful for the identification of these common skin cancers (3). Semitranslucent areas in dermoscopic images of BCC also display the smooth, jelly‐like appearance of the clinical counterpart (WV Stoecker et al, unpublished data).…”

Section: Histogram Texture Measuresmentioning

confidence: 99%

Detection of basal cell carcinoma using color and histogram measures of semitranslucent areas

Stoecker

Gupta

Shrestha

et al. 2009

Skin Research and Technology

View full text Add to dashboard Cite

Background-Semitranslucency, defined as a smooth, jelly-like area with varied, near-skin-tone color, can indicate a diagnosis of basal cell carcinoma (BCC) with high specificity. This study sought to analyze potential areas of semitranslucency with histogram-derived texture and color measures to discriminate BCC from non-semitranslucent areas in non-BCC skin lesions.Methods-For 210 dermoscopy images, the areas of semitranslucency in 42 BCCs and comparable areas of smoothness and color in 168 non-BCCs were selected manually. Six color measures and six texture measures were applied to the semitranslucent areas of the BCC and the comparable areas in the non-BCC images.Results-Receiver operating characteristic (ROC) curve analysis showed that the texture measures alone provided greater separation of BCC from non-BCC than the color measures alone. Statistical analysis showed that the four most important measures of semitranslucency are three histogram measures: contrast, smoothness, and entropy, and one color measure: blue chromaticity. Smoothness is the single most important measure. The combined 12 measures achieved a diagnostic accuracy of 95.05% based on area under the ROC curve.Conclusion-Texture and color analysis measures, especially smoothness, may afford automatic detection of basal cell carcinoma images with semitranslucency.

show abstract

An expert diagnostic program for dermatology

Cited by 20 publications

References 2 publications

Nondermatoscopic digital imaging of pigmented lesions

Nondermatoscopic digital imaging of pigmented lesions

A Survey on Deep Learning for Skin Lesion Segmentation

Detection of basal cell carcinoma using color and histogram measures of semitranslucent areas

Contact Info

Product

Resources

About