Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images

Ali, Abder-Rahman; Li, Jingpeng; O’Shea, Sally J.

doi:10.1371/journal.pone.0234352

Cited by 24 publications

(25 citation statements)

References 35 publications

(38 reference statements)

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“…Such network will be used in detecting the skin lesion border irregularity and will be compared with its standard neural network counterpart. Detecting the other features (i.e., ACD) is explained in our other work (10).…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images

Ali

Kanwal

et al. 2020

Front. Med.

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Skin lesion border irregularity, which represents the B feature in the ABCD rule, is considered one of the most significant factors in melanoma diagnosis. Since signs that clinicians rely on in melanoma diagnosis involve subjective judgment including visual signs such as border irregularity, this deems it necessary to develop an objective approach to finding border irregularity. Increased research in neural networks has been carried out in recent years mainly driven by the advances of deep learning. Artificial neural networks (ANNs) or multilayer perceptrons have been shown to perform well in supervised learning tasks. However, such networks usually don't incorporate information pertaining the ambiguity of the inputs when training the network, which in turn could affect how the weights are being updated in the learning process and eventually degrading the performance of the network when applied on test data. In this paper, we propose a fuzzy multilayer perceptron (F-MLP) that takes the ambiguity of the inputs into consideration and subsequently reduces the effects of ambiguous inputs on the learning process. A new optimization function, the fuzzy gradient descent, has been proposed to reflect those changes. Moreover, a type-II fuzzy sigmoid activation function has also been proposed which enables finding the range of performance the fuzzy neural network is able to attain. The fuzzy neural network was used to predict the skin lesion border irregularity, where the lesion was firstly segmented from the skin, the lesion border extracted, border irregularity measured using a proposed measure vector, and using the extracted border irregularity measures to train the neural network. The proposed approach outperformed most of the state-of-the-art classification methods in general and its standard neural network counterpart in particular. However, the proposed fuzzy neural network was more time-consuming when training the network.

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Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images

Ali

Kanwal

et al. 2020

Front. Med.

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“…Ali et al [52] built a vector of three measures to find skin lesion asymmetry: SIFT based similarity, projection profiles and skewness. In particular, the authors split the extracted skin lesion vertically and horizontally across the center of the images into four halves, and used SIFT (Scale-invariant feature transform) [53] to measure the similarity between each opposite half.…”

Section: Asymmetrymentioning

confidence: 99%

“…This approach was previously used in [47], [48] and [51]. The same rationale was used in [52] to determine the suspicious colors present in each skin lesion. However, the CIELab color space is used in [52] which is more representable of the human perception than the RGB color space.…”

Section: Color Variegationmentioning

confidence: 99%

“…The same rationale was used in [52] to determine the suspicious colors present in each skin lesion. However, the CIELab color space is used in [52] which is more representable of the human perception than the RGB color space. Moreover, the derived CIELab values of suspicious colors in [52] are based on the color distribution of the dataset used, making it more accurate in determining the melanoma suspicious colors.…”

Section: Color Variegationmentioning

confidence: 99%

“…However, the CIELab color space is used in [52] which is more representable of the human perception than the RGB color space. Moreover, the derived CIELab values of suspicious colors in [52] are based on the color distribution of the dataset used, making it more accurate in determining the melanoma suspicious colors. A range of colors is formed for black, dark brown and light brown, as opposed to only being represented by one value.…”

Section: Color Variegationmentioning

confidence: 99%

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Automating the ABCD Rule for Melanoma Detection: A Survey

Ali

Yang

2020

IEEE Access

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The ABCD rule is a simple framework that physicians, novice dermatologists and nonphysicians can use to learn about the features of melanoma in its early curable stage, enhancing thereby the early detection of melanoma. Since the interpretation of the ABCD rule traits is subjective, different solutions have been proposed in literature to tackle such subjectivity and provide objective evaluations to the different traits. This paper reviews the main contributions in literature towards automating asymmetry, border irregularity, color variegation and diameter, where the different methods involved have been highlighted. This survey could serve as an essential reference for researchers interested in automating the ABCD rule.INDEX TERMS Image processing, machine learning, melanoma detection.GUANG YANG received the M.Sc. degree in vision imaging and virtual environments from the Department of Computer Science, University College London, in 2006, and the Ph.D. degree in medical image analysis jointly from the CMIC,

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Evaluating perceptual and semantic interpretability of saliency methods: A case study of melanoma

Bokadia

Yang

et al. 2022

Applied AI Letters

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In order to be useful, XAI explanations have to be faithful to the AI system they seek to elucidate and also interpretable to the people that engage with them. There exist multiple algorithmic methods for assessing faithfulness, but this is not so for interpretability, which is typically only assessed through expensive user studies. Here we propose two complementary metrics to algorithmically evaluate the interpretability of saliency map explanations. One metric assesses perceptual interpretability by quantifying the visual coherence of the saliency map. The second metric assesses semantic interpretability by capturing the degree of overlap between the saliency map and textbook features-features human experts use to make a classification. We use a melanoma dataset and a deep-neural network classifier as a case-study to explore how our two interpretability metrics relate to each other and a faithfulness metric. Across six commonly used saliency methods, we find that none achieves high scores across all three metrics for all test images, but that different methods perform well in different regions of the data distribution. This variation between methods can be leveraged to consistently achieve high interpretability and faithfulness by using our metrics to inform saliency mask selection on a case-by-case basis. Our interpretability metrics provide a new way to evaluate saliency-based explanations and allow for the adaptive combination of saliency-based explanation methods.

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Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images

Cited by 24 publications

References 35 publications

A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images

A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images

Automating the ABCD Rule for Melanoma Detection: A Survey

Evaluating perceptual and semantic interpretability of saliency methods: A case study of melanoma

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