Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults

Ruffano, Lavinia Ferrante di; Takwoingi, Yemisi; Dinnes, Jacqueline; Chuchu, Naomi; Bayliss, Sue; Davenport, Clare; Matin, Rubeta; Godfrey, Kathie; O'Sullivan, Colette; Gulati, Abha; Chan, Sue Ann; Durack, Alana; O’Çonnell, Susan; Gardiner, Matthew; Bamber, Jeffrey C.; Deeks, Jonathan J; Williams, Hywel C

doi:10.1002/14651858.cd013186

Cited by 83 publications

(59 citation statements)

References 316 publications

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“…A meta‐analysis of dermoscopy and spectroscopy‐based computer‐assisted diagnosis (CAD) techniques for diagnosing skin cancer in adults found, for the diagnosis of melanoma, 90.1% sensitivity and 74.3% specificity for digital dermoscopy‐based CAD across 22 studies and 92.9% sensitivity and 43.6% specificity for multispectral imaging‐based CAD across 8 studies (Ferrante di Ruffano et al., 2018). The authors concluded that the evidence for computer‐assisted diagnosis of melanoma is poor, especially as most studies excluded lesions clinically diagnosed as benign, and evidence for individual systems was too limited to draw conclusions on which might be preferred for practice (Ferrante di Ruffano et al., 2018).…”

Section: Computer‐assisted Diagnosis and Artificial Intelligencementioning

confidence: 99%

The role of technology in melanoma screening and diagnosis

Young

Vora

Cortez

et al. 2020

Pigment Cell Melanoma Res

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Melanoma presents challenges for timely and accurate diagnosis. Expert panels have issued risk‐based screening guidelines, with recommended screening by visual inspection. To assess how recent technology can impact the risk/benefit considerations for melanoma screening, we comprehensively reviewed non‐invasive visual‐based technologies. Dermoscopy increases lesional diagnostic accuracy for both dermatologists and primary care providers; total body photography and sequential digital dermoscopic imaging also increase diagnostic accuracy, are supported by automated lesion detection and tracking, and may be best suited to use by dermatologists for longitudinal follow‐up. Specialized imaging modalities using non‐visible light technology have unproven benefit over dermoscopy and can be limited by cost, access, and training requirements. Mobile apps facilitate image capture and lesion tracking. Teledermatology has good concordance with face‐to‐face consultation and increases access, with increased accuracy using dermoscopy. Deep learning models can surpass dermatologist accuracy, but their clinical utility has yet to be demonstrated. Technology‐aided diagnosis may change the calculus of screening; however, well‐designed prospective trials are needed to assess the efficacy of these different technologies, alone and in combination to support refinement of guidelines for melanoma screening.

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Section: Computer‐assisted Diagnosis and Artificial Intelligencementioning

confidence: 99%

The role of technology in melanoma screening and diagnosis

Young

Vora

Cortez

et al. 2020

Pigment Cell Melanoma Res

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“…In dermatology, both diagnosis and monitoring of skin lesions have relied mainly on visual inspection and other non-invasive evaluations, invasive procedures are avoided because they can destroy the lesions and make it impossible to carry out a clinical monitoring of its evolution [6]. In highly selected patient populations, computer assisted diagnosis methods have demonstrated high sensitivity and may be useful as a back-up diagnostic aid for specialists to reduce the risk of missing melanomas [7]. One of the non-invasive methods employed in diagnosis and monitoring is the use of confocal microscopes available for clinical use, which provide sharp images because they capture only the light from the plane of the sample in focus.…”

Section: Introductionmentioning

confidence: 99%

Melanoma diagnosis using deep learning techniques on dermatoscopic images

et al. 2021

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Background Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that is able to detect the presence of melanoma via a dermatoscopic image of lesions and/or skin pigmentation can be a very useful tool in the area of medical diagnosis. Methods Among state-of-the-art methods used for automated or computer assisted medical diagnosis, attention should be drawn to Deep Learning based on Convolutional Neural Networks, wherewith segmentation, classification and detection systems for several diseases have been implemented. The method proposed in this paper involves an initial stage that automatically crops the region of interest within a dermatoscopic image using the Mask and Region-based Convolutional Neural Network technique, and a second stage based on a ResNet152 structure, which classifies lesions as either “benign” or “malignant”. Results Training, validation and testing of the proposed model was carried out using the database associated to the challenge set out at the 2017 International Symposium on Biomedical Imaging. On the test data set, the proposed model achieves an increase in accuracy and balanced accuracy of 3.66% and 9.96%, respectively, with respect to the best accuracy and the best sensitivity/specificity ratio reported to date for melanoma detection in this challenge. Additionally, unlike previous models, the specificity and sensitivity achieve a high score (greater than 0.8) simultaneously, which indicates that the model is good for accurate discrimination between benign and malignant lesion, not biased towards any of those classes. Conclusions The results achieved with the proposed model suggest a significant improvement over the results obtained in the state of the art as far as performance of skin lesion classifiers (malignant/benign) is concerned.

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“…Accuracy can be further increased using additional data from novel non-invasive imaging systems. Commonly used technologies include reflectance confocal microscopy (RCM), optical coherence tomography, fluorescence imaging, high-frequency ultrasound and multispectral imaging, such as diffuse reflectance spectrophotometry imaging [ 23 , 24 , 25 ]. Some, such as RCM, have already proven to be useful in cost-benefit analyses and as an aid for dermatoscopic evaluation of atypical lesions [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…Diffuse reflectance spectrophotometry (DRS) records light reflected off melanin, collagen, hemoglobin and other cutaneous chromophores [ 28 ], producing images of the visible and near-infrared spectrum (400–1000 nm) [ 23 ]. The light used can penetrate the papillary dermis, reaching 2 mm lesion depth.…”

Section: Introductionmentioning

confidence: 99%

“…Examples of such systems include SIAscope and MelaFind. A recent Cochrane review put the sensitivity and specificity of multispectral imaging CAD systems at 0.929 (95% CI, 0.837 to 0.971) and 0.436 (95% CI, 0.248 to 0.645), respectively [ 23 ], with one RCT SIAscopy study in an unreferred population achieving a specificity of 0.725 with comparable sensitivities [ 29 ]. Combining spectrophotometry with other quantitative imaging modalities could yield even better diagnostic accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters

et al. 2020

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Dermatoscopy, high-frequency ultrasonography (HFUS) and spectrophotometry are promising quantitative imaging techniques for the investigation and diagnostics of cutaneous melanocytic tumors. In this paper, we propose the hybrid technique and automatic prognostic models by combining the quantitative image parameters of ultrasonic B-scan images, dermatoscopic and spectrophotometric images (melanin, blood and collagen) to increase accuracy in the diagnostics of cutaneous melanoma. The extracted sets of various quantitative parameters and features of dermatoscopic, ultrasonic and spectrometric images were used to develop the four different classification models: logistic regression (LR), linear discriminant analysis (LDA), support vector machine (SVM) and Naive Bayes. The results were compared to the combination of only two techniques out of three. The reliable differentiation between melanocytic naevus and melanoma were achieved by the proposed technique. The accuracy of more than 90% was estimated in the case of LR, LDA and SVM by the proposed method.

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Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults

Abstract: Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults.

Cited by 83 publications

References 316 publications

The role of technology in melanoma screening and diagnosis

The role of technology in melanoma screening and diagnosis

Melanoma diagnosis using deep learning techniques on dermatoscopic images

Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters

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