Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering

Nida, Nudrat; Irtaza, Aun; Javed, Ali; Yousaf, Muhammad Haroon; Mahmood, Muhammad Tariq

doi:10.1016/j.ijmedinf.2019.01.005

Cited by 159 publications

(86 citation statements)

References 37 publications

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“…In both studies, the models have shown competitive or outperformed the dermatologists. Other efforts have been made using deep learning to detect skin cancer, such as ensemble of models [32,33], feature aggregation of different models [34], among others [35,36,37,38]. Most of these works are based on dermoscopy images, mainly for two reasons: 1) there is an open well-known dataset provided by the International Skin Imaging Collaboration (ISIC) [39]; 2) obtaining a dataset of clinical images of skin cancer is a hard task.…”

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confidence: 99%

The impact of patient clinical information on automated skin cancer detection

Pacheco

Krohling

2020

Computers in Biology and Medicine

131

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Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we present a new dataset that contains clinical images, acquired from smartphones, and patient clinical information of the skin lesions. Next, we introduce a straightforward approach to combine the clinical data and the images using different well-known deep learning models. These models are applied to the presented dataset using only the images and combining them with the patient clinical information. We present a comprehensive study to show the impact of the clinical data on the final predictions. The results obtained by combining both sets of information show a general improvement of around 7% in the balanced accuracy for all models. In addition, the statistical test indicates significant differences between the models with and without considering both data. The improvement achieved shows the potential of using patient clinical information in skin cancer detection and indicates that this piece of information is important to leverage skin cancer detection systems.

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confidence: 99%

The impact of patient clinical information on automated skin cancer detection

Pacheco

Krohling

2020

Computers in Biology and Medicine

131

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“…The ABCD rule, which uses Symmetry (a), Edge (b), Color (c), and Dimension (d) as descriptors to discriminate between benign and malignant melanoma, [18] has been widely used in the literature due to its excellent results [19]. However, it is interesting to analyze the importance of each descriptor in our designed multilayer perceptron.…”

Section: Descriptors' Importance Analysismentioning

confidence: 99%

“…The American Academy of Dermatology uses the ABCD rule to identify melanoma. The ABCD rule was introduced in 1985 [18,19] and states that the four main descriptors for identification are:…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 shows relevant research in the literature focused on the detection of melanoma using image processing. [18] 95.5% undefined [41] The aforementioned studies have an efficiency between 70% and 96%, and they are only designed for one type of image (dermatological or simple), mostly working with RGB and CIELAB color space, and symmetry evaluation is performed in a similar way or the descriptor is not considered.…”

Section: Introductionmentioning

confidence: 99%

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A High-Accuracy Mathematical Morphology and Multilayer Perceptron-Based Approach for Melanoma Detection

et al. 2020

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According to the World Health Organization (WHO), melanoma is the most severe type of skin cancer and is the leading cause of death from skin cancer worldwide. Certain features of melanoma include size, shape, color, or texture changes of a mole. In this work, a novel, robust and efficient method for the detection and classification of melanoma in simple and dermatological images is proposed. It is achieved by using HSV (Hue, Saturation, Value) color space along with mathematical morphology and a Gaussian filter to detect the region of interest and estimate four descriptors: symmetry, edge, color, and size. Although these descriptors have been used for several years, the way they are computed for this proposal is one of the things that enhances the results. Subsequently, a multilayer perceptron is employed to classify between malignant and benign melanoma. Three datasets of simple and dermatological images commonly used in the literature were employed to train and evaluate the performance of the proposed method. According to k-fold cross-validation, the method outperforms three state-of-art works, achieving an accuracy of 98.5% and 98.6%, a sensitivity of 96.68% and 98.05%, and a specificity of 98.15%, and 98.01%, in simple and dermatological images, respectively. The results have proven that its use as an assistive device for the detection of melanoma would improve reliability levels compared to conventional methods.

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“…handwriting, face, behavior...), recommender systems or image classification. Recent works show that deep networks are being a powerful tools for medical image analysis [10], and therefore for melanoma classification [12,17].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Digital Image by Deep Learning for Melanoma Diagnosis

Thurnhofer‐Hemsi

Domínguez

2019

Advances in Computational Intelligence

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Image classification is an important task in many medical applications, in order to achieve an adequate diagnostic of different lesions. Melanoma is a frequent kind of skin cancer, which most of them can be detected by visual exploration. Heterogeneity and database size are the most important difficulties to overcome in order to obtain a good classification performance. In this work, a deep learning based method for accurate classification of wound regions is proposed. Raw images are fed into a Convolutional Neural Network (CNN) producing a probability of being a melanoma or a non-melanoma. Alexnet and GoogLeNet were used due to their well-known effectiveness. Moreover, data augmentation was used to increase the number of input images. Experiments show that the compared models can achieve high performance in terms of mean accuracy with very few data and without any preprocessing.

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Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering

Cited by 159 publications

References 37 publications

The impact of patient clinical information on automated skin cancer detection

The impact of patient clinical information on automated skin cancer detection

A High-Accuracy Mathematical Morphology and Multilayer Perceptron-Based Approach for Melanoma Detection

Analyzing Digital Image by Deep Learning for Melanoma Diagnosis

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