Automated Skin Lesion Preprocessing Model of Dermoscopic Images Towards Melanoma Detection

2021 ◽  
Author(s):  
Sudhir Sawarkar ◽  
Saumya Salian
Keyword(s):  
Skin Lesion ◽  
Melanoma Detection

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.

CryptInfer: Enabling Encrypted Inference on Skin Lesion Images for Melanoma Detection

2021 ◽  
Author(s):  
Nayna Jain ◽  
Karthik Nandakumar ◽  
Nalini Ratha ◽  
Sharath Pankanti ◽  
Uttam Kumar
Keyword(s):  
Skin Lesion ◽  
Melanoma Detection

scholarly journals Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

2020 ◽  
Vol 6 (12) ◽  
pp. 129
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Neural Networks ◽  
Skin Lesion ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Image Features ◽  
Ensemble Classification ◽  
Deep Convolutional Neural Networks ◽  
Melanoma Detection ◽  
Statistical Measures ◽  
Different Types

Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.

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