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.

scholarly journals A Deep Learning-Based Transfer Learning Framework for the Early Detection and Classification of Dermoscopic Images of Melanoma

2021 ◽  
Vol 14 (3) ◽  
pp. 1231-1247
Author(s):  
Lokesh Singh ◽  
Rekh Ram Janghel ◽  
Satya Prakash Sahu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Skin Lesions ◽  
Learning Framework ◽  
Melanoma Detection ◽  
Pigmented Skin Lesions ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Purpose:Less contrast between lesions and skin, blurriness, darkened lesion images, presence of bubbles, hairs are the artifactsmakes the issue challenging in timely and accurate diagnosis of melanoma. In addition, huge similarity amid nevus lesions and melanoma pose complexity in investigating the melanoma even for the expert dermatologists. Method: In this work, a computer-aided diagnosis for melanoma detection (CAD-MD) system is designed and evaluated for the early and accurate detection of melanoma using thepotentials of machine, and deep learning-based transfer learning for the classification of pigmented skin lesions. The designed CAD-MD comprises of preprocessing, segmentation, feature extraction and classification. Experiments are conducted on dermoscopic images of PH2 and ISIC 2016 publicly available datasets using machine learning and deep learning-based transfer leaning models in twofold: first, with actual images, second, with augmented images. Results:Optimal results are obtained on augmented lesion images using machine learning and deep learning models on PH2 and ISIC-16 dataset. The performance of the CAD-MD system is evaluated using accuracy, sensitivity, specificity, dice coefficient, and jacquard Index. Conclusion:Empirical results show that using the potentials of deep learning-based transfer learning model VGG-16 has significantly outperformed all employed models with an accuracy of 99.1% on the PH2 dataset.

Classification of Skin Lesion Using (Segmentation) Shape Feature Detection

2020 ◽  
pp. 221-228
Author(s):  
Satheesha T.Y.
Keyword(s):  
Skin Lesion ◽  
Feature Detection ◽  
Indentation Depth ◽  
Skin Lesions ◽  
Traditional Concept ◽  
Melanoma Detection ◽  
Shape Signature ◽  
Significant Performance ◽  
Wavelet Descriptor

Malignant melanoma has caused countless deaths in recent years. Many calculation methods have been created for automatic melanoma detection. In this chapter, based on the traditional concept of shape signature and convex hull, an improved boundary description shape signature is developed. The convex defect-based signature (CDBS) proposed in this paper scans contour irregularities and is applied to skin lesion classification in macroscopic images. Border irregularities of skin lesions are the predominant criteria for ABCD (asymmetry, border, color, and diameter) to distinguish between melanoma and nonmelanoma. The performance of the CDBS is compared with popular shape descriptors: shape signature, indentation depth function, invariant elliptic Fourier descriptor (IEFD), and rotation invariant wavelet descriptor (RIWD), where the proposed descriptor shows better results. Multilayer perceptron neural network is used as a classifier in this work. Experimental results show that the proposed approach achieves significant performance with mean accuracy of 90.49%.

AI-Enabled Support System for Melanoma Detection and Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 58-75
Author(s):  
Vivek Sen Saxena ◽  
Prashant Johri ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Lesion ◽  
Performance Metrics ◽  
Similarity Index ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Lesion Area ◽  
Melanoma Detection ◽  
Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

Melanoma Detection by Meta-Heuristically-Optimized MLP Parameters Using Non-Dermatoscopy Images

2021 ◽  
Vol 12 (4) ◽  
pp. 177-200
Author(s):  
Soumen Mukherjee ◽  
Arunabha Adhikari ◽  
Madhusudan Roy
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Skin Lesion ◽  
Multilayer Perceptron ◽  
Binary Classification ◽  
Ranking Algorithm ◽  
Swarm Optimization ◽  
Melanoma Detection ◽  
Class 1

This paper represents a scheme of melanoma detection using handcrafted feature set with meta-heuristically optimized multilayer perceptron (MLP) parameters. Features including shape, color, and texture are extracted from camera images of skin lesion collected from University of Waterloo database. The features are used in two different ways for binary classification of the data into benign and malignant class. 1) The extracted features are ranked on their relevance using ReleifF ranking algorithm and also converted into PCA components and ranked according to their variance. Best result is obtained with 50 best ranked raw features with accuracy of 87.1%. 2) All 1,888 features are fed to an MLP with two hidden layers, with number of neurons optimized by two different metaheuristic algorithms, namely particle swarm optimization (PSO) and simulated annealing (SA) separately. The latter method is found to be more efficient, and an accuracy of 88.38%, sensitivity of 92.22%, and specificity of 83.07% are achieved by PSO, which is better in comparison with the latest research on this dataset.

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.

scholarly journals Melanoma Detection in Dermoscopic Images using Color Features

2019 ◽  
Vol 12 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Sameena Pathan ◽  
Vatsal Aggarwal ◽  
K. Gopalakrishna Prabhu ◽  
P. C. Siddalingaswamy
Keyword(s):  
Region Of Interest ◽  
Ensemble Classifiers ◽  
Dynamic Selection ◽  
Melanoma Detection ◽  
Color Features ◽  
Melanocytic Lesions ◽  
Average Accuracy ◽  
Malignant Lesions

Color is considered to be a major characteristic feature that is used for distinguishing benign and malignant melanocytic lesions. Most of malignant melanomas are characterized by the presence of six suspicious colors inspired from the ABCD dermoscopic rule. The presence of these suspicious colors histopathologically indicates the presence of melanin in the deeper layers of the epidermis and dermis. The objective of the proposed work is to evaluate the role of color features, a set of fifteen color features have been extracted from the region of interest to determine the role of color in malignancy detection. Further, a set of ensemble classifiers with dynamic selection techniques are used for classification of the extracted features, yielding an average accuracy of 87.5% for classifying benign and malignant lesions.

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 Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-22 ◽  
Author(s):  
Ammara Masood ◽  
Adel Ali Al-Jumaily
Keyword(s):  
Skin Cancer ◽  
State Of The Art ◽  
Comparative Assessment ◽  
Future Research ◽  
Feature Extraction And Selection ◽  
Diagnostic Models ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Diagnostic Support

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique’s performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.

scholarly journals Dermoscopy Case of the Month Combined Nevus - a Case Report

2017 ◽  
Vol 9 (2) ◽  
pp. 63-65
Author(s):  
Andrija Jović ◽  
Nataša Vidović ◽  
Danijela Popović ◽  
Zorana Zlatanović ◽  
Slađana Cekić ◽  
...  
Keyword(s):  
Case Report ◽  
Skin Lesion ◽  
Female Patient ◽  
Surgical Excision ◽  
Skin Lesions ◽  
Lumbar Region ◽  
Dermoepidermal Junction ◽  
Melanocytic Lesions ◽  
Different Types ◽  
The Right

Abstract Combined nevi are melanocytic lesions composed of two or more distinct types of melanocytic populations within the same lesion. Different types of combined nevi may form bizarrely shaped, multicolored skin lesions, making them one of the greatest melanoma mimickers. We report a 48-year-old female patient with suspicious skin lesion in the left lumbar region. Clinically, there was an oval, slightly asymmetrical lesion measuring 6 x 4 mm, showing multiple colors and shades of brown and black. A dermoscopic examination revealed a brown-bluish coloration in the right part of the lesion, while a fine pigment network with perifollicular halo was found in the left part of the lesion, suggesting the diagnosis of a combined nevus. Histological examination showed a poorly circumscribed proliferation of dendritic melanocytes in the superficial and deep dermis and proliferation of melanocytes in the dermoepidermal junction. A surgical excision of the tumor was performed, in order to confirm the dermoscopic findings. In conclusion, dermoscopy is useful in differentiating combined nevi from other melanocytic lesions.

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