scholarly journals Neural network classification system for pigmented skin neoplasms with preliminary hair removal in photographs

2021 ◽  
Vol 5 (45) ◽  
pp. 728-735
Author(s):  
P.A. Lyakhov ◽  
U.A. Lyakhova
Keyword(s):  
Neural Network ◽  
Classification System ◽  
Skin Neoplasms ◽  
Skin Lesions ◽  
Diagnostic Efficiency ◽  
Automated Classification ◽  
Neural Network Classification ◽  
Pigmented Lesions ◽  
Classification Of Images ◽  
Visual Diagnosis

The article proposes a neural network classification system for pigmented skin neoplasms with a preliminary processing stage to remove hair from the images. The main difference of the proposed system is the use of the stage of preliminary image processing to identify the location of the hair and their further removal. This stage allows you to prepare dermatoscopic images for further analysis in order to carry out automated classification and diagnosis of pigmented skin lesions. Modeling was carried out using the MatLAB R2020b software package on clinical dermatoscopic images from the international open archive ISIC Melanoma Project. The proposed system made it possible to increase the recognition accuracy of pigmented skin lesion images in 10 diagnostically important categories up to 80.81%. The use of the proposed system for the recognition and classification of images of dermatoscopic pigmented lesions by specialists will make it possible to increase the diagnostic efficiency in comparison with methods of visual diagnosis, and will also allow starting treatment at an earlier stage of the disease, which directly affects the survival and recovery rates for patients.

scholarly journals DEEP LEARNING BASED SKIN LESIONS DIAGNOSIS

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 81-85 ◽  
Author(s):  
D. A. Gavrilov ◽  
N. N. Shchelkunov ◽  
A. V. Melerzanov
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Intelligent Systems ◽  
State Of The Art ◽  
Skin Lesions ◽  
Deep Convolutional Neural Networks ◽  
Melanoma Diagnosis ◽  
Malignant Skin ◽  
Diagnosis Accuracy ◽  
Visual Diagnosis

<p><strong>Abstract.</strong> Melanoma is one of the most virulent lesions of human’s skin. The visual diagnosis accuracy of melanoma directly depends on the doctor’s qualification and specialization. State-of-the-art solutions in the field of image processing and machine learning allows to create intelligent systems based on artificial convolutional neural network exceeding human’s rates in the field of object classification, including the case of malignant skin lesions. This paper presents an algorithm for the early melanoma diagnosis based on artificial deep convolutional neural networks. The algorithm proposed allows to reach the classification accuracy of melanoma at least 91%.</p>

scholarly journals A PRELIMARY APPROACH FOR THE AUTOMATED RECOGNITION OF MALIGNANT MELANOMA

2011 ◽  
Vol 23 (2) ◽  
pp. 121 ◽  
Author(s):  
Ezzeddine Zagrouba ◽  
Walid Barhoumi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Color Image ◽  
Skin Lesions ◽  
Benign Lesions ◽  
Data Set ◽  
Pigmented Lesions ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural

In this work, we are motivated by the desire to classify skin lesions as malignants or benigns from color photographic slides of the lesions. Thus, we use color images of skin lesions, image processing techniques and artificial neural network classifier to distinguish melanoma from benign pigmented lesions. As the first step of the data set analysis, a preprocessing sequence is implemented to remove noise and undesired structures from the color image. Second, an automated segmentation approach localizes suspicious lesion regions by region growing after a preliminary step based on fuzzy sets. Then, we rely on quantitative image analysis to measure a series of candidate attributes hoped to contain enough information to differentiate melanomas from benign lesions. At last, the selected features are supplied to an artificial neural network for classification of tumor lesion as malignant or benign. For a preliminary balanced training/testing set, our approach is able to obtain 79.1% of correct classification of malignant and benign lesions on real skin lesion images.

NEURAL NETWORK CLASSIFICATION OF SYMMETRICAL AND NONSYMMETRICAL IMAGES USING NEW MOMENTS WITH HIGH NOISE TOLERANCE

1999 ◽  
Vol 13 (08) ◽  
pp. 1233-1250
Author(s):  
R. PALANIAPPAN ◽  
P. RAVEENDRAN ◽  
SIGERU OMATU
Keyword(s):  
Neural Network ◽  
Random Noise ◽  
Noise Tolerance ◽  
Neural Network Classification ◽  
Reference Centre ◽  
Different Types ◽  
Moment Functions ◽  
Classification Of Images ◽  
Invariant Properties

The classification of images using regular or geometric moment functions suffers from two major problems. First, odd orders of central moments give zero value for images with symmetry in the x and/or y directions and symmetry at centroid. Secondly, these moments are very sensitive to noise especially for higher order moments. In this paper, a single solution is proposed to solve both these problems. The solution involves the computation of the moments from a reference point other than the image centroid. The new reference centre is selected such that the invariant properties like translation, scaling and rotation are still maintained. In this paper, it is shown that the new proposed moments can solve the symmetrical problem. Next, we show that the new proposed moments are less sensitive to Gaussian and random noise as compared to two different types of regular moments derived by Hu.6 Extensive experimental study using a neural network classification scheme with these moments as inputs are conducted to verify the proposed method.

scholarly journals Convolutional Neural Network for Skin Lesion Classification: Understanding the Fundamentals Through Hands-On Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Marta Cullell-Dalmau ◽  
Sergio Noé ◽  
Marta Otero-Viñas ◽  
Ivan Meić ◽  
Carlo Manzo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Building Blocks ◽  
Computer Code ◽  
Skin Lesions ◽  
Hands On ◽  
Hands On Learning ◽  
Technological Barriers ◽  
Classification Of Images

Deep learning architectures for the classification of images have shown outstanding results in a variety of disciplines, including dermatology. The expectations generated by deep learning for, e.g., image-based diagnosis have created the need for non-experts to become familiar with the working principles of these algorithms. In our opinion, getting hands-on experience with these tools through a simplified but accurate model can facilitate their understanding in an intuitive way. The visualization of the results of the operations performed by deep learning algorithms on dermatological images can help students to grasp concepts like convolution, even without an advanced mathematical background. In addition, the possibility to tune hyperparameters and even to tweak computer code further empower the reach of an intuitive comprehension of these processes, without requiring advanced computational and theoretical skills. This is nowadays possible thanks to recent advances that have helped to lower technical and technological barriers associated with the use of these tools, making them accessible to a broader community. Therefore, we propose a hands-on pedagogical activity that dissects the procedures to train a convolutional neural network on a dataset containing images of skin lesions associated with different skin cancer categories. The activity is available open-source and its execution does not require the installation of software. We further provide a step-by-step description of the algorithm and of its functions, following the development of the building blocks of the computer code, guiding the reader through the execution of a realistic example, including the visualization and the evaluation of the results.

scholarly journals System for neural network recognition of malignant pigmented skin neoplasms with image pre-processing

2021 ◽  
Vol 2052 (1) ◽  
pp. 012023
Author(s):  
U A Lyakhova ◽  
P A Lyakhov ◽  
R I Abdulkadirov ◽  
G A Efimenko ◽  
S A Romanov ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Software Package ◽  
Skin Neoplasms ◽  
Recognition System ◽  
Training Data ◽  
Processing Stage ◽  
The Neural Network ◽  
Visual Diagnosis

Abstract The article presents a system for the recognition of malignant pigmented skin neoplasms with a preliminary processing stage. Image pre-processing consists of removing hair structures from images, as well as resizing images and their further augmentation. Augmentation made it possible to increase the variety of training data, balance the number of images in different categories, and avoid retraining the neural network. The modeling was carried out using the MatLab R2020b software package for solving technical calculations on clinical dermatoscopic images from the international open archive ISIC Melanoma Project. The proposed system for the recognition of malignant pigmented skin neoplasms made it possible to increase the accuracy of image classification up to 80.55%. The use of the proposed recognition system will make it possible to increase the efficiency and quality of diagnosis, in comparison with the methods of visual diagnosis.

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