Improvement in the diagnosis of melanoma and dysplastic lesions by introducing ABCD-PDT features and a hybrid classifier

Zakeri, Arezoo; Hokmabadi, Alireza

doi:10.1016/j.bbe.2018.03.005

Cited by 21 publications

(7 citation statements)

References 34 publications

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“…Finally, these features were classified using an SVM. Zakeri et al [133] proposed a CAD system to differentiate between melanoma and dysplastic lesions. They enhanced the grey-level co-occurrence matrix to extract features.…”

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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“…This guide consists of looking for specific characteristics that allow detecting the asymmetry (A) of a lesion; the type of border (B) if it is irregular, uneven, or blurred; the color variation (C), with reddish, whitish, and bluish being the most dangerous; and the length of the diameter (D) of the lesion [17]. In [18][19][20][21], different methodologies for melanoma detection use the ABCD rule. Unfortunately, this type of system development is hampered by several challenges, such as the lack of data sets with detailed clinical criteria information, or the subtlety of some diagnostic criteria that makes them difficult to detect.…”

Section: Related Jobsmentioning

confidence: 99%

Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

et al. 2022

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Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The mortality rates of melanoma are associated with its high potential for metastasis in later stages, spreading to other body sites such as the lungs, bones, or the brain. Thus, early detection and diagnosis are closely related to survival rates. Computer Aided Design (CAD) systems carry out a pre-diagnosis of a skin lesion based on clinical criteria or global patterns associated with its structure. A CAD system is essentially composed by three modules: (i) lesion segmentation, (ii) feature extraction, and (iii) classification. In this work, a methodology is proposed for a CAD system development that detects global patterns using texture descriptors based on statistical measurements that allow melanoma detection from dermoscopic images. Image analysis was carried out using spatial domain methods, statistical measurements were used for feature extraction, and a classifier based on cellular automata (ACA) was used for classification. The proposed model was applied to dermoscopic images obtained from the PH2 database, and it was compared with other models using accuracy, sensitivity, and specificity as metrics. With the proposed model, values of 0.978, 0.944, and 0.987 of accuracy, sensitivity and specificity, respectively, were obtained. The results of the evaluated metrics show that the proposed method is more effective than other state-of-the-art methods for melanoma detection in dermoscopic images.

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“…After the ROI has been determined, several features are extracted to support the classification. Geometric and shape features, 17,18 color features, 19,20 and texture features 21,22 are all widely used for classification. However, due to numerous factors, including the nature of the disease, the appearance of a mole, the diseased region, and the distance of capture, most of these features cannot be extracted as stated in.…”

Section: Related Workmentioning

confidence: 99%

HS‐UNET‐ID: An approach for human skin classification integrating between UNET and improved dense convolutional network

Nguyen

Luong

Phan

et al. 2022

Int J Imaging Syst Tech

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The Sun's ultraviolet radiation, toxic chemicals from industry, and other factors can cause problems to human skin. These factors have boosted the risk of humans contracting skin cancer. In identifying and detecting signals of skin cancer diseases, recent advancements in deep learning techniques significantly improve the accuracy of diagnosis. This work delves into processing techniques for human skin images with MorphologyEx (blackhat), thresholding, semantic segmentation with UNET, and several famous convolutional neural network architectures to improve skin classification performance. If skin cancer can be stopped in the first stage, the survival rate of the patient could be more than 95%, while it drops to just 5% if the detection is carried lately in Stage IV4. Due to its seriousness, the healthcare and research community has received considerable attention, and its most important goal is to be able to diagnose it at an early stage. However, the challenge due to the similarity in melanocytic and non-melanoma skin lesions is inevitable. We achieved skin classification performances of 0.8560, 0.9672, 0.8063, and 0.8560 measured by overall accuracy, area under curve, Matthews correlation coefficient, and F1-score, respectively. The proposed approach has been deployed into applications on Mobile and Web platforms.

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Improvement in the diagnosis of melanoma and dysplastic lesions by introducing ABCD-PDT features and a hybrid classifier

Cited by 21 publications

References 34 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

Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

HS‐UNET‐ID: An approach for human skin classification integrating between UNET and improved dense convolutional network

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