Detection and Classification of Skin Lesions using Probability Map-based Region Growing with BA-KNN Classifier (Preprint)

2021 ◽  
Author(s):  
Sivaraj S ◽  
Dr.R. Malmathanraj
Keyword(s):  
Skin Lesion ◽  
Region Growing ◽  
Skin Lesions ◽  
Classification Approach ◽  
Probability Map ◽  
Knn Classifier ◽  
Knn Classification ◽  
Pigmented Skin Lesions ◽  
Thresholding Algorithm

BACKGROUND Melanoma is one of the most hazardous existing diseases, and is a kind of threatening pigmented skin lesion. Appropriate automated diagnosis of skin lesions and the categorization of melanoma may be exceptionally enhancing premature identification of melanomas. OBJECTIVE However, Models of categorization based on deterministic skin lesion may influence multi-dimensional nonlinear problem provokes inaccurate and ineffective categorization. This research presents a novel hybrid BA-KNN classification approach for pigmented skin lesions in dermoscopy images. METHODS In the first step, the skin lesion is preprocessed via automatic preprocessing algorithm together with a fusion hair detection and removal strategy. Also, a new probability map based region growing and optimal thresholding algorithm is integrated in this system to enhance the rate of accuracy. RESULTS Moreover, to attain better efficacy, an estimate of ABCD as well as geometric features are considered during the feature extraction to describe the malignancy of the lesion. CONCLUSIONS The evaluation of the experiment reveals the efficiency of the proposed approach on dermoscopy images with better accuracy

scholarly journals The Study of Usefulness of a Set of Fractal Parameters to Build Classes of Disease Units Based on Images of Pigmented Skin Lesions

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1773
Author(s):  
Monika Styła ◽  
Tomasz Giżewski
Keyword(s):  
Skin Lesion ◽  
Fractal Analysis ◽  
Skin Lesions ◽  
Diagnostic Systems ◽  
Kohonen Network ◽  
Network Learning ◽  
Fractal Parameters ◽  
Fully Automatic ◽  
Pigmented Skin Lesions

Dermatoscopic images are also increasingly used to train artificial neural networks for the future to provide fully automatic diagnostic systems capable of determining the type of pigmented skin lesion. Therefore, fractal analysis was used in this study to measure the irregularity of pigmented skin lesion surfaces. This paper presents selected results from individual stages of preliminary processing of the dermatoscopic image on pigmented skin lesion, in which fractal analysis was used and referred to the effectiveness of classification by fuzzy or statistical methods. Classification of the first unsupervised stage was performed using the method of analysis of scatter graphs and the fuzzy method using the Kohonen network. The results of the Kohonen network learning process with an input vector consisting of eight elements prove that neuronal activation requires a larger learning set with greater differentiation. For the same training conditions, the final results are at a higher level and can be classified as weaker. Statistics of factor analysis were proposed, allowing for the reduction in variables, and the directions of further studies were indicated.

Comparing the capabilities of transfer learning models to detect skin lesion in humans

2020 ◽  
Vol 234 (10) ◽  
pp. 1083-1093 ◽  
Author(s):  
Aditi Singhal ◽  
Ramesht Shukla ◽  
Pavan Kumar Kankar ◽  
Saurabh Dubey ◽  
Sukhjeet Singh ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Transfer Learning ◽  
Wiener Filter ◽  
Skin Lesions ◽  
Automated Classification ◽  
Convolutional Networks ◽  
Image Quantization ◽  
Malignant Skin ◽  
Effective Diagnosis

Effective diagnosis of skin tumours mainly relies on the analysis of the characteristics of the lesion. Automatic detection of malignant skin lesion has become a mandatory task to reduce the risk of human deaths and increase their survival. This article proposes a study of skin lesion classification using transfer learning approach. The transfer learning model uses four different state-of-the-art architectures, namely Inception v3, Residual Networks (ResNet 50), Dense Convolutional Networks (DenseNet 201) and Inception Residual Networks (Inception ResNet v2). These models are trained under the dataset comprising seven different classes of skin lesions. The skin lesion images are pre-processed using image quantization, grayscaling and the Wiener filter before final training step. These models are compared for performance evaluation on different metrics. The present study shows the efficacy of the methodology for automated classification of lesion images.

scholarly journals A Mobile-Based Prototype for Skin Lesion Analysis: Towards a Patient-Oriented Design Approach

2013 ◽  
Vol 9 (S8) ◽  
pp. 27 ◽  
Author(s):  
Luís Rosado ◽  
Maria João Vasconcelos ◽  
Márcia Ferreira
Keyword(s):  
Skin Lesion ◽  
Active Role ◽  
Skin Lesions ◽  
Self Monitoring ◽  
Promising Tool ◽  
Processing Power ◽  
Skin Cancers ◽  
Lesion Analysis ◽  
New Generation

The wide spreading of the new generation of smartphones, with significant improvements in terms of image acquisition and processing power, is opening up the possibility of new approaches for skin lesion monitoring. Mobile Teledermatology appears nowadays as a promising tool with the potential to empower patients to adopt an active role in managing their own skin health status, while facilitates the early diagnosis of skin cancers. The main objective of this work is to create a mobile-based prototype for skin lesions analysis with patient-oriented features and functionalities. The presented self-monitoring system collects, processes and storages information of skin lesions through the automatic extraction and classification of specific visual features. The algorithms used to extract and classify these features are briefly described, as well as the overall system architecture and functionalities.

SEGMENTATION OF DERMATOSCOPIC IMAGES USED FOR COMPUTER-AIDED DIAGNOSIS OF MELANOMA

2010 ◽  
Vol 10 (02) ◽  
pp. 213-223
Author(s):  
MHAMMED MESSADI ◽  
ABDELHAFID BESSAID ◽  
A. TALEB-AHMED
Keyword(s):  
Methodological Approach ◽  
Region Growing ◽  
Skin Lesions ◽  
Skin Tumor ◽  
Border Detection ◽  
Fast Approach ◽  
Malignant Skin ◽  
Pigmented Skin Lesions ◽  
Aided Diagnosis ◽  
Related Mortality

In this paper, a methodological approach to the segmentation of tumours skin lesions in dermoscopy images is presented. Melanoma is the most malignant skin tumor, growing in melanocytes, the cells responsible for pigmentation. This type of cancer is nowadays increasing rapidly, its related mortality rate increases by more modest and inversely proportional to the thickness of the tumor. This rate can be decreased by an earlier detection and better prevention. In dermatoscopic images, the segmentation is essential to characterize the information shape of the lesion and also to locate the tumor for analysis. In this domain, we have evaluated several techniques for the segmentation of dermatoscopic images. All these methods do not exactly separate the lesion from the background. In this work a fast approach in border detection of dermoscopy pigmented skin lesions images based on the region growing algorithm is presented. This method is tested on a set of 60 dermoscopy images. The obtained results show that the presented method achieves both fast and accurate border detection.

scholarly journals Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions

2015 ◽  
Vol 24 (6) ◽  
pp. 061104 ◽  
Author(s):  
Víctor González-Castro ◽  
Johan Debayle ◽  
Yanal Wazaefi ◽  
Mehdi Rahim ◽  
Caroline Gaudy-Marqueste ◽  
...  
Keyword(s):  
Skin Lesions ◽  
Pigmented Skin Lesions

scholarly journals Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Joanna Jaworek-Korjakowska ◽  
Paweł Kłeczek
Keyword(s):  
Skin Lesion ◽  
Skin Lesions ◽  
New Approach ◽  
Melanoma Detection ◽  
Specificity And Sensitivity ◽  
Melanocytic Lesions ◽  
Feature Extraction And Selection ◽  
Aided Diagnosis ◽  
Automatic Algorithm

Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet.Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification.Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly.Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision.

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