A methodological approach to classify typical and atypical pigment network patterns for melanoma diagnosis

Pathan, Sameena; Prabhu, Kartik; Siddalingaswamy, P. C.

doi:10.1016/j.bspc.2018.03.017

Cited by 29 publications

(7 citation statements)

References 20 publications

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“…Finally, these features were classified using an SVM. Pathan et al [134] proposed a detection system for pigment networks and differentiated between typical and atypical network patterns. In [135], laser-induced breakdown spectroscopy was used with a combination of statistical methods to distinguish between the soft tissue of the skin.…”

Section: Traditional Machine Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

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Section: Traditional Machine Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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“…On the other hand, another group of researchers applied segmentation methods to discard background and unneeded features [12]. In fact, the procedures of segmentation and classification are based on low-level features with low discrimination capabilities which led to bad results [13].…”

Section: Introductionmentioning

confidence: 99%

Classification of skin lesions using transfer learning and augmentation with Alex-net

2019

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Skin cancer is one of most deadly diseases in humans. According to the high similarity between melanoma and nevus lesions, physicians take much more time to investigate these lesions. The automated classification of skin lesions will save effort, time and human life. The purpose of this paper is to present an automatic skin lesions classification system with higher classification rate using the theory of transfer learning and the pre-trained deep neural network. The transfer learning has been applied to the Alex-net in different ways, including fine-tuning the weights of the architecture, replacing the classification layer with a softmax layer that works with two or three kinds of skin lesions, and augmenting dataset by fixed and random rotation angles. The new softmax layer has the ability to classify the segmented color image lesions into melanoma and nevus or into melanoma, seborrheic keratosis, and nevus. The three well-known datasets, MED-NODE, Derm (IS & Quest) and ISIC, are used in testing and verifying the proposed method. The proposed DCNN weights have been fine-tuned using the training and testing dataset from ISIC in addition to 10-fold cross validation for MED-NODE and DermIS—DermQuest. The accuracy, sensitivity, specificity, and precision measures are used to evaluate the performance of the proposed method and the existing methods. For the datasets, MED-NODE, Derm (IS & Quest) and ISIC, the proposed method has achieved accuracy percentages of 96.86%, 97.70%, and 95.91% respectively. The performance of the proposed method has outperformed the performance of the existing classification methods of skin cancer.

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“…Differently, segmentation is adopted to isolate the foreground elements from the background ones [ 6 ]. Consequently, the segmentation includes low-level features with a low representational power that provides unsatisfactory results [ 7 ]. In recent years, deep learning has become an effective solution for the extraction of significant features on large data.…”

Section: Introductionmentioning

confidence: 99%

Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

Manzo

Pellino²

2020

J. Imaging

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Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.

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A methodological approach to classify typical and atypical pigment network patterns for melanoma diagnosis

Cited by 29 publications

References 20 publications

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Classification of skin lesions using transfer learning and augmentation with Alex-net

Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

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