Multi-Class CNN for Classification of Multispectral and Autofluorescence Skin Lesion Clinical Images

Lihacova, Ilze; Bondarenko, Andrey; Chizhov, Yuriy; Uteshev, Dilshat; Bliznuks, Dmitrijs; Kiss, Norbert; Lihachev, Alexey

doi:10.3390/jcm11102833

Cited by 12 publications

(8 citation statements)

References 41 publications

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“…Combining autofluorescence with the chromophore mapping approach, a new prototype for skin tissue analysis was created Lihacova et al in [55]. The device was used in several studies to find significant differences between melanoma and non-melanoma lesions of the skin [13,[55][56][57]. A parameter p' was computed by the following formula:…”

Section: Prototypesmentioning

confidence: 99%

“…In addition, a particle analysis was computed to identify the particle with high fluorescence values and the previous cumulative ratio was summed with the resulting product between particle number and area percentage (ratio of the area of particle with high florescence values and area of the lesion) [13]. Automated classification of different types of skin malformations was also tested using a convolutional neural network trained and validated on a reduced dataset of multispectral images [57]. The MSI device played an essential role because it made it possible to build a collection of different skin lesions: melanoma-like, pigmented benign, hyperkeratotic, and other lesions, as well as non-melanoma skin cancer such as basal cell carcinoma.…”

Section: Prototypesmentioning

confidence: 99%

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Multispectral Imaging for Skin Diseases Assessment—State of the Art and Perspectives

Ilișanu

Moldoveanu

2023

Sensors

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Skin optical inspection is an imperative procedure for a suspicious dermal lesion since very early skin cancer detection can guarantee total recovery. Dermoscopy, confocal laser scanning microscopy, optical coherence tomography, multispectral imaging, multiphoton laser imaging, and 3D topography are the most outstanding optical techniques implemented for skin examination. The accuracy of dermatological diagnoses attained by each of those methods is still debatable, and only dermoscopy is frequently used by all dermatologists. Therefore, a comprehensive method for skin analysis has not yet been established. Multispectral imaging (MSI) is based on light–tissue interaction properties due to radiation wavelength variation. An MSI device collects the reflected radiation after illumination of the lesion with light of different wavelengths and provides a set of spectral images. The concentration maps of the main light-absorbing molecules in the skin, the chromophores, can be retrieved using the intensity values from those images, sometimes even for deeper-located tissues, due to interaction with near-infrared light. Recent studies have shown that portable and cost-efficient MSI systems can be used for extracting skin lesion characteristics useful for early melanoma diagnoses. This review aims to describe the efforts that have been made to develop MSI systems for skin lesions evaluation in the last decade. We examined the hardware characteristics of the produced devices and identified the typical structure of an MSI device for dermatology. The analyzed prototypes showed the possibility of improving the specificity of classification between the melanoma and benign nevi. Currently, however, they are rather adjuvants tools for skin lesion assessment, and efforts are needed towards a fully fledged diagnostic MSI device.

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Section: Prototypesmentioning

confidence: 99%

Section: Prototypesmentioning

confidence: 99%

Multispectral Imaging for Skin Diseases Assessment—State of the Art and Perspectives

Ilișanu

Moldoveanu

2023

Sensors

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“…They proved to be effective in many computational regimes 23,33 . Current state-of-the-art architectures are understudied in the biomedical domain, although recent studies prove their potential in diverse applications [33][34][35][36][37] . Therefore, we nd it valuable to verify their superiority over commonly used architectures in medical image classi cation.…”

Section: Modelsmentioning

confidence: 99%

Counteracting data bias and class imbalance – towards useful and reliable retinal disease recognition system

Karanowski

Chłopowiec

Skrzypczak

et al. 2023

Preprint

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Fundus images play a fundamental role in the early screening of eye diseases. On the other hand, as deep learning provides an accurate classification of medical images, it is natural to apply such techniques for fundus images. There are many developments in deep learning for such image data but are often burdened with the same common mistakes. Training data are biased, not diverse and hidden to the public. Algorithms classify diseases, which suitability for screening could be questioned. Therefore, in our research, we consolidate most of the available public data of fundus images (pathological and non-pathological) taking into consideration only image data relevant to the most distressing retinal diseases. Next, we apply some well-known state-of-the-art deep learning models for the classification of the consolidated image data addressing class imbalance problem occurring in the dataset and clinical usage. In a conclusion, we present our classification results for diabetic retinopathy, glaucoma, and age- related macular degeneration disease, which are urgent problem of ageing populations in developed countries.

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“…Another approach applied to the analysis of Raman spectra is neural networks. They are used as a classifier of diseases, as for example was undertaken in [10][11][12]. The main disadvantage is that the use of neural networks requires a lot of training and test datasets.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Curve Resolution Alternating Least Squares Analysis of In Vivo Skin Raman Spectra

Матвеева

Bratchenko

Khristoforova

et al. 2022

Sensors

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In recent years, Raman spectroscopy has been used to study biological tissues. However, the analysis of experimental Raman spectra is still challenging, since the Raman spectra of most biological tissue components overlap significantly and it is difficult to separate individual components. New methods of analysis are needed that would allow for the decomposition of Raman spectra into components and the evaluation of their contribution. The aim of our work is to study the possibilities of the multivariate curve resolution alternating least squares (MCR-ALS) method for the analysis of skin tissues in vivo. We investigated the Raman spectra of human skin recorded using a portable conventional Raman spectroscopy setup. The MCR-ALS analysis was performed for the Raman spectra of normal skin, keratosis, basal cell carcinoma, malignant melanoma, and pigmented nevus. We obtained spectral profiles corresponding to the contribution of the optical system and skin components: melanin, proteins, lipids, water, etc. The obtained results show that the multivariate curve resolution alternating least squares analysis can provide new information on the biochemical profiles of skin tissues. Such information may be used in medical diagnostics to analyze Raman spectra with a low signal-to-noise ratio, as well as in various fields of science and industry for preprocessing Raman spectra to remove parasitic components.

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Multi-Class CNN for Classification of Multispectral and Autofluorescence Skin Lesion Clinical Images

Cited by 12 publications

References 41 publications

Multispectral Imaging for Skin Diseases Assessment—State of the Art and Perspectives

Multispectral Imaging for Skin Diseases Assessment—State of the Art and Perspectives

Counteracting data bias and class imbalance – towards useful and reliable retinal disease recognition system

Multivariate Curve Resolution Alternating Least Squares Analysis of In Vivo Skin Raman Spectra

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