Method of cleaning hair structures for intellectual image classification of skin neoplasms

Method of cleaning hair structures for intellectual image classification of skin neoplasms

The main problem in the application of artificial intelligence in the field of dermatology is the low level of accuracy of recognition and classification systems. This paper proposes a method for identifying and cleaning hair, as well as evaluating the effectiveness of the method using a pre-trained...

Сохранить в:

書目詳細資料
Главные авторы:	Lyakhova, U. A., Ляхова, У. А., Lyakhov, P. A., Ляхов, П. А.
格式:	Статья
語言:	English
出版:	Institute of Electrical and Electronics Engineers Inc. 2021
主題:	Convolutional neural networks Dermatoscopic images Digital image processing Hair removal Medical image processing; Melanoma Pigmented skin lesions
在線閱讀:	https://dspace.ncfu.ru/handle/20.500.12258/18092
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

相似書籍

Neural network classification system for pigmented skin neoplasms with preliminary hair removal in photographs
由: Lyakhov, P. A., и др.
出版: (2021)

System for the recognizing of pigmented skin lesions with fusion and analysis of heterogeneous data based on a multimodal neural network
由: Lyakhov, P. A., и др.
出版: (2022)

Semantic Segmentation System of Pigmented Skin Lesions Based on Convolutional Neural Networks
由: Fedorenko, V. V., и др.
出版: (2022)

Neural network classification of dermatoscopic images of pigmented skin lesions
由: Lyakhov, P. A., и др.
出版: (2022)

Method for determining skin lesions from images using neural network
由: Lyakhova, U. A., и др.
出版: (2020)

System for neural network recognition of malignant pigmented skin neoplasms with image pre-processing
由: Lyakhova, U. A., и др.
出版: (2021)

Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects
由: Lyakhova, U. A., и др.
出版: (2024)

Cloud-Based Service for Recognizing Pigmented Skin Lesions Using a Multimodal Neural Network System
由: Lyakhova, U. A., и др.
出版: (2023)

RNS-based image processing
由: Chervyakov, N. I., и др.
出版: (2018)

RNS-based image processing
由: Chervyakov, N. I., и др.
出版: (2018)

Implementation of Smoothing Image Filtering in the Residue Number System
由: Chervyakov, N. I., и др.
出版: (2019)

Efficiency analysis of the image impulse noise cleaning using median filters with weighted central element
由: Chervyakov, N. I., и др.
出版: (2018)

Low-bit hardware implementation of DWT for 3D medical images processing
由: Lyakhov, P. A., и др.
出版: (2020)

High-quality 3D medical imaging by wavelet filters with reduced coefficients bit-width
由: Chervyakov, N. I., и др.
出版: (2020)

Neural Network Skin Cancer Recognition with a Modified Cross-Entropy Loss Function
由: Lyakhova, U. A., и др.
出版: (2023)

Analysis of the quantization noise in discrete wavelet transform filters for 3D medical imaging
由: Chervyakov, N. I., и др.
出版: (2020)

Two methods of adaptive median filtering of impulse noise in images
由: Chervyakov, N. I., и др.
出版: (2018)

New method for detecting and removing random-valued impulse noise from images
由: Lyakhov, P. A., и др.
出版: (2023)

High-speed smoothing filter in the residue number system
由: Chervyakov, N. I., и др.
出版: (2018)

High-performance hardware 3D medical imaging using wavelets in the residue number system
由: Chervyakov, N. I., и др.
出版: (2020)

Method of oriented contour detection on image using lorentz function
由: Lyakhov, P. A., и др.
出版: (2020)

A new method for adaptive median filtering of images
由: Lyakhov, P. A., и др.
出版: (2019)

Development of digital image processing algorithms based on the Winograd method in general form and analysis of their computational complexity
由: Lyakhov, P. A., и др.
出版: (2023)

Analysis of the quantization noise of linear time-invariant filters for image processing
由: Lyakhov, P. A., и др.
出版: (2019)

PROSPECTS FOR CREATING COMPUTER- AND MRI-BASED 3D MODELS OF INTERNAL ORGANS IN EMERGENCY SURGERY AND RESUSCITATION
由: Baklanov, I. S., и др.
出版: (2021)

Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise
由: Orazaev, A. R., и др.
出版: (2023)

On the algorithmic complexity of digital image processing filters with winograd calculations
由: Lyakhov, P. A., и др.
出版: (2022)

Uncoded Video Data Stream Denoising in a Binary Symmetric Channel
由: Orazaev, A. R., и др.
出版: (2023)

Distortion level analysis of a 2D median filter with a weighted central element
由: Kiladze, M. R., и др.
出版: (2022)

How country image affects intention to visit a destination: Evidence from russian tourists visiting switzerland
由: Belozerova, O. I., и др.
出版: (2019)

Control of the latent image formed as a combination of variously oriented textures
由: Zharkih, A. A., и др.
出版: (2019)

On the Computational Complexity of 2D Filtering by Winograd Method
由: Lyakhov, P. A., и др.
出版: (2022)

Increasing of convolutional neural network performance using residue number system
由: Chervyakov, N. I., и др.
出版: (2018)

Quantization noise of multilevel discrete wavelet transform filters in image processing
由: Chervyakov, N. I., и др.
出版: (2019)

Multimodal Analysis of Unbalanced Dermatological Data for Skin Cancer Recognition
由: Lyakhov, P. A., и др.
出版: (2023)

Image processing algorithms application for the modeling biogeochemical cycles of the Azov Sea
由: Timofeeva, E. F., и др.
出版: (2022)

Analysis of the quantization noise in discrete wavelet transform filters for image processing
由: Chervyakov, N. I., и др.
出版: (2018)

Analysis of the quantization noise in discrete wavelet transform filters for image processing
由: Chervyakov, N. I., и др.
出版: (2018)

Английский язык в сфере культурологии учебное пособие
由: Бардинская, Т. Р.

Hardware implementation of a convolutional neural network using calculations in the residue number system
由: Chervyakov, N. I., и др.
出版: (2019)