A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

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

doi:10.7717/peerj-cs.268

Cited by 41 publications

(32 citation statements)

References 68 publications

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“…Popular classifiers, such as CNNs used to classify skin lesions into benign or malignant lesions based on various approaches, were presented in [ 52 , 53 ]. The parameter B in the ABCDE rule, i.e., the skin lesion border detection and border irregularity estimation, was used for the input features for a CNN, which uses binary classification (i.e., melanoma vs. non-melanoma) [ 52 ]. They reported that all the regular borders were identified correctly, and only three irregular borders were classified as regular; thus, an overall accuracy of 93.6% was obtained.…”

Section: Resultsmentioning

confidence: 99%

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

et al. 2021

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In the automatic detection framework, there have been many attempts to develop models for real-time melanoma detection. To effectively discriminate benign and malign skin lesions, this work investigates sixty different architectures of the Feedforward Back Propagation Network (FFBPN), based on shape asymmetry for an optimal structural design that includes both the hidden neuron number and the input data selection. The reason for the choice of shape asymmetry was based on the 5–10% disagreement between dermatologists regarding the efficacy of asymmetry in the diagnosis of malignant melanoma. Asymmetry is quantified based on lesion shape (contour), moment of inertia of the lesion shape and histograms. The FFBPN has a high architecture flexibility, which indicates it as a favorable tool to avoid the over-parameterization of the ANN and, equally, to discard those redundant input datasets that usually result in poor test performance. The FFBPN was tested on four public image datasets containing melanoma, dysplastic nevus and nevus images. Experimental results on multiple benchmark data sets demonstrate that asymmetry A2 is a meaningful feature for skin lesion classification, and FFBPN with 16 neurons in the hidden layer can model the data without compromising prediction accuracy.

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Section: Resultsmentioning

confidence: 99%

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

et al. 2021

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“…Deep learning has grown and been used in many areas in the last decade, for example in the field of object recognition (Ghoreyshi, AkhavanPour & Bossaghzadeh, 2020;Ali et al, 2020), speech recognition (Deng, Hinton & Kingsbury, 2013;Li, Baucom & Georgiou, 2020), anomaly detection (Zhao et al, 2018), feature extraction (Lin, Nie & Ma, 2017;Rajaraman et al, 2018), auto-encoding (Pu et al, 2016). Also, in cases where deep learning along with machine learning has been used for text analysis and sentiment analysis, good results have been obtained (Tang, Qin & Liu, 2015;Severyn & Moschitti, 2015).…”

Section: Related Workmentioning

confidence: 99%

Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

Shumaly

Yazdinejad

Guo

2021

PeerJ Computer Science

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Sentiment analysis plays a key role in companies, especially stores, and increasing the accuracy in determining customers’ opinions about products assists to maintain their competitive conditions. We intend to analyze the users’ opinions on the website of the most immense online store in Iran; Digikala. However, the Persian language is unstructured which makes the pre-processing stage very difficult and it is the main problem of sentiment analysis in Persian. What exacerbates this problem is the lack of available libraries for Persian pre-processing, while most libraries focus on English. To tackle this, approximately 3 million reviews were gathered in Persian from the Digikala website using web-mining techniques, and the fastText method was used to create a word embedding. It was assumed that this would dramatically cut down on the need for text pre-processing through the skip-gram method considering the position of the words in the sentence and the words’ relations to each other. Another word embedding has been created using the TF-IDF in parallel with fastText to compare their performance. In addition, the results of the Convolutional Neural Network (CNN), BiLSTM, Logistic Regression, and Naïve Bayes models have been compared. As a significant result, we obtained 0.996 AUC and 0.956 F-score using fastText and CNN. In this article, not only has it been demonstrated to what extent it is possible to be independent of pre-processing but also the accuracy obtained is better than other researches done in Persian. Avoiding complex text preprocessing is also important for other languages since most text preprocessing algorithms have been developed for English and cannot be used for other languages. The created word embedding due to its high accuracy and independence of pre-processing has other applications in Persian besides sentiment analysis.

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“…Fig. 2 shows samples of skin lesions with regular and irregular borders [54]. In [30], a dermatologist was asked to score 60 skin tumor images as being regular or irregular (regular: 14, irregular: 46).…”

Section: Border Irregularitymentioning

confidence: 99%

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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A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

Cited by 41 publications

References 68 publications

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

Automating the ABCD Rule for Melanoma Detection: A Survey

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