eSkin: Study on the Smartphone Application for Early Detection of Malignant Melanoma

Jaworek-Korjakowska, Joanna; Kłeczek, Paweł

doi:10.1155/2018/5767360

Cited by 26 publications

(15 citation statements)

References 16 publications

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“…In Saudi Arabia and Philippine, there is an extremely small percentage of health workers using mHealth applications and a low level of awareness of such technologies. Again, the review illustrates that mHealth devices provide accurate diagnostic procedures for a few disease conditions across LMICs to promote quality healthcare services [ 133 , 136 ]. The review further illustrates some risks associated with the use of mHealth such as non-adherence to antiretroviral treatment, the wrong amount of medications, inaccurate data interpretation, wrong insertion of data by patients, and others across many LMICs [ 106 , 107 , 108 ].…”

Section: Discussionmentioning

confidence: 99%

“…Jaworek-Korjakowska et al. reported that mHealth applications were also utilized to accurately diagnose or detect Malignant Melanoma cases among individuals in Poland [ 136 ]. A study in Madagascar has shown that mHealth devices were deployed to accurately diagnose soil-transmitted helminth infections to promote healthcare delivery [ 137 ].…”

Section: Overviewmentioning

confidence: 99%

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Mobile health applications for disease screening and treatment support in low-and middle-income countries: A narrative review

Osei

Mashamba-Thompson

2021

Heliyon

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The advances in mobile technologies and applications are driving the transformation in health services delivery globally. Mobile phone penetration is increasing exponentially in low-and middle-income countries, hence using mobile phones for healthcare services could reach more people in resource-limited settings than the traditional forms of healthcare provision. The review presents recent literature on facilitators and barriers of implementing mHealth for disease screening and treatment support in low-and middle-income countries. We searched for relevant literature from the following electronic databases: MEDLINE; CINAHL with full text via EBSCOhost; Science Direct; PubMed; Google Scholar and Web of Science using the keywords for relevant studies. We searched for published studies from 2015 to August 2020 with no language limitations. A total of 721 articles identified, 125 articles met the inclusion criteria and were included in the qualitative synthesis. The review demonstrates relevant facilitators for the implementation of mHealth, which includes knowledge, attitudes, and perceptions of stakeholders on the use of mHealth and the performance of mHealth for disease diagnosis in low and-middle-income countries. Barriers and challenges hindering the implementation of mHealth applications were also identified. We proposed a framework for improving the implementation of mHealth for disease screening and treatment support in low-and middle-income countries.

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Section: Discussionmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Mobile health applications for disease screening and treatment support in low-and middle-income countries: A narrative review

Osei

Mashamba-Thompson

2021

Heliyon

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“…The human skin is the largest body organ and can be agonised from different factors like sun (Ultraviolet) radiations, tanning, lifestyle, smoking, alcohol usage, physical activities, viruses and working environment (Jaworek-Korjakowska & Kleczek, 2018;Salem, Azar, & Tokajian, 2018). These factors compromise its integrity and have a profound, devastating impact on its well-being.…”

Section: Introductionmentioning

confidence: 99%

Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques

Hameed

Shabut

Ghosh

et al. 2020

Expert Systems with Applications

151

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Skin diseases remain a major cause of disability worldwide and contribute approximately 1.79% of the global burden of disease measured in disability-adjusted life years. In the United Kingdom alone, 60% of the population suffer from skin diseases during their lifetime. In this paper, we propose an intelligent digital diagnosis scheme to improve the classification accuracy of multiple diseases. A Multi-Class Multi-Level (MCML) classification algorithm inspired by the "divide and conquer" rule is explored to address the research challenges. The MCML classification algorithm is implemented using traditional machine learning and advanced deep learning approaches. Improved techniques are proposed for noise removal in the traditional machine learning approach. The proposed algorithm is evaluated on 3672 classified images, collected from different sources and the diagnostic accuracy of 96.47% is achieved. To verify the performance of the proposed algorithm, its metrics are compared with the Multi-Class Single-Level classification algorithm which is the main algorithm used in most of the existing literature. The results also indicate that the MCML classification algorithm is capable of enhancing the classification performance of multiple skin lesions.

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“…In general, the skin tumors are classified into malignant melanomas and non-melanoma, and they rank according to the cell in which they originated [2]. The most common…”

Section: Introductionmentioning

confidence: 99%

“…non-melanomas are basal cell carcinoma and squamous cell carcinoma. Regarding the malignant skin diseases, Melanoma is the most dangerous skin cancer but less frequently; if it is detected early, it can be curable, having a death rate of 85% of all fatal skin cancer cases [2], [3]. In particular, the main features of the skin lesions for Melanoma are asymmetrical shape, irregular edges, various shades of pigmentation, and its diameter can be greater than 6 mm.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Diagnosis of Skin Lesions Using the Fourier Spectral Information of Images on Additive Color Model by Artificial Neural Network

2021

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This article presents a new methodology to diagnostics ten types of skin lesions based on the image' s Fourier spectral information in an additive color model. All spectral information and correlation coefficients between the skin lesions classes conform the input signals to an Artificial Neural Network. In general, the results show the well-defined classification for all the skin lesions classes based on the high values for Accuracy, Precision, Sensitivity, and Specificity metrics performance and a reduced images misclassification percentage (≈5.9%) for the Testing sub-dataset, and less for Training (≈2.8%) and Validation (≈5.6%) sub-dataset even considering the strange objects, not-clarity, and black sections in some images analyzed. The general achieved classification Accuracy, Precision, Sensitivity, and Specificity percentages of the proposed method are 99.33 %, 94.16 %, 92.9 %, and 99.63 %, respectively. In particular, the skin lesions related to Basal Cell Carcinoma, Seborrhoeic Keratosis, and Melanocytic Nevus present the best performance regarding the Receiver Operating Characteristics, while the Pyogenic Granuloma was the worst classified.INDEX TERMS Artificial neural networks, biomedical computing, image processing, medical diagnostic imaging, Fourier spectral analysis.

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eSkin: Study on the Smartphone Application for Early Detection of Malignant Melanoma

Cited by 26 publications

References 16 publications

Mobile health applications for disease screening and treatment support in low-and middle-income countries: A narrative review

Mobile health applications for disease screening and treatment support in low-and middle-income countries: A narrative review

Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques

Multi-Class Diagnosis of Skin Lesions Using the Fourier Spectral Information of Images on Additive Color Model by Artificial Neural Network

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