Improving the diagnostic accuracy of dysplastic and melanoma lesions using the decision template combination method

Faal, Maryam; Baygi, Mohammad Hossein Miran; Kabir, Ehsanollah

doi:10.1111/j.1600-0846.2012.00617.x

Cited by 15 publications

(6 citation statements)

References 32 publications

(72 reference statements)

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“…The relevance of the features selected for input is stressed, as the ensemble accuracy decreases with the increase of feature number. This topic is also highlighted by Faal et al, after achieving better classification results with an ensemble of k‐NN, SVM and Linear discriminant analysis (LDA) algorithms with different feature inputs for the different classifiers, as oppose to the same shape, colour and texture components. The impact of input vectors was also tested by Rastgoo et al with the implementation of an ensemble learning with random forest (RF) and weighted combination constructed with RF, SVM and LDA, where the combination of several features achieve higher specificity results (94%), instead of the use of a single characteristic.…”

Section: Resultsmentioning

confidence: 95%

“…Most publications concerning the use of classifiers for skin cancer detection seem to highlight the importance of feature extraction and selection stages to attain the best results. [19][20][21][22]25,26,28,49,[63][64][65][66][67] Thus, it is expected further research in this area, focusing on image analysis and processing, in preference of new machine learning strategies.…”

Section: Discussion Of Trends and Future Challeng E Smentioning

confidence: 99%

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The role of AI classifiers in skin cancer images

Magalhães

Mendes

Vardasca

2019

Skin Research and Technology

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Background The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from image analysis and processing. However, the integration and understanding of these additional parameters can be a challenging task for physicians, so artificial intelligence (AI) methods can be implemented to assist in this process. This bibliographic research was performed with the goal of assessing the current applications of AI algorithms as an assistive tool in skin cancer diagnosis, based on information retrieved from different imaging modalities. Materials and methods The bibliography databases ISI Web of Science, PubMed and Scopus were used for the literature search, with the combination of keywords: skin cancer, skin neoplasm, imaging and classification methods. Results The search resulted in 526 publications, which underwent a screening process, considering the established eligibility criteria. After screening, only 65 were qualified for revision. Conclusion Different imaging modalities have already been coupled with AI methods, particularly dermoscopy for melanoma recognition. Learners based on support vector machines seem to be the preferred option. Future work should focus on image analysis, processing stages and image fusion assuring the best possible classification outcome.

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Section: Resultsmentioning

confidence: 95%

Section: Discussion Of Trends and Future Challeng E Smentioning

confidence: 99%

The role of AI classifiers in skin cancer images

Magalhães

Mendes

Vardasca

2019

Skin Research and Technology

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“…By varying image processing techniques and training algorithms of ANN, the accuracy can be improved for this system. Maryam Faal et al [47] present a multi-classifier systems. The best multiple classifier system was selected as the system with the highest classification accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

“…A feed-forward multilayer network is employed [46]. The network is trained with known values [47]. The network performs decision making afterward training [48].…”

Section: Fig(5) Dwt On Malignant Image 44 Lesion Classificationmentioning

confidence: 99%

Assessment of Segmentation techniques for skin cancer detection

Srividya¹,

Arulmozhi²

2020

IRJASH

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Skin cancer appears to be the most common among all tumors throughout the globe. The initial finding of skin cancer can be alleviated. Late detection leads to fatal. A human inquiry is thought-provoking. The biopsy procedure is agonizing, so computerized examination of skin cancer turns out to be noteworthy. A prevalent literature survey is carried out to study the State-of-art procedures for skin cancer diagnosis. Segmentation of skin lesion is a crucial task due to several features like the existence of hair, illumination difference, irregular skin color, and multiple unnatural skin regions. This paper recommends a comparison of various segmentation techniques and k-means clustering algorithms to segment the lesion. Several methodologies have been anticipated to determine skin cancer. The features can be resolved by familiarizing an advanced method for segmenting the skin lesion from macroscopic images based on the discrete wavelet transformation.

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“…Several methods have been developed for automatic or semi-automatic PSL delineation on melanoma images which mostly rely on color as being the most important information. Many systems select a simple scalar feature such as the intensity [6], or the B and b channel values of RGB and CIE L*a*b* color spaces [7] which better discriminate in most dermoscopic images. Others use the CIE L*u*v* color model as a feature space [8], or its principal components [9] and luminance [10] information.…”

Section: Introductionmentioning

confidence: 99%

New data model for graph-cut segmentation: Application to automatic melanoma delineation

Kéchichian¹,

Gong²,

Revenu³

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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We propose a new data model for graph-cut image segmentation, defined according to probabilities learned by a classification process. Unlike traditional graph-cut methods, the data model takes into account not only color but also texture and shape information. For melanoma images, we also introduce skin chromophore features and automatically derive "seed" pixels used to train the classifier from a coarse initial segmentation. On natural images, our method successfully segments objects having similar color but different texture. Its application to melanoma delineation compares favorably to manual delineation and related graph-cut segmentation methods.

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Improving the diagnostic accuracy of dysplastic and melanoma lesions using the decision template combination method

Abstract: The results show that the proposed method significantly increases the diagnostic accuracy of dysplastic and melanoma lesions compared with a single classifier. The total classification rate is also improved.

Cited by 15 publications

References 32 publications

The role of AI classifiers in skin cancer images

The role of AI classifiers in skin cancer images

Assessment of Segmentation techniques for skin cancer detection

New data model for graph-cut segmentation: Application to automatic melanoma delineation

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