A Study on the Fusion of Pixels and Patient Metadata in CNN-Based Classification of Skin Lesion Images

2020 ◽  
pp. 191-208
Author(s):  
Fabrizio Nunnari ◽  
Chirag Bhuvaneshwara ◽  
Abraham Obinwanne Ezema ◽  
Daniel Sonntag
Keyword(s):  
Skin Lesion

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.

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

2019 ◽  
Vol 82 (6) ◽  
pp. 741-763 ◽  
Author(s):  
Muhammad Attique Khan ◽  
Tallha Akram ◽  
Muhammad Sharif ◽  
Tanzila Saba ◽  
Kashif Javed ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Saliency Map

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 Classification of Skin Lesion with Hair and Artifacts Removal using Black-hat Morphology and Total Variation

2021 ◽  
Vol 10 (1) ◽  
pp. 597-604
Author(s):  
Adil H. Khan ◽  
D.N.F. Awang Iskandar ◽  
Jawad F. Al-Asad ◽  
Samir El-Nakla
Keyword(s):  
Skin Lesion ◽  
Total Variation ◽  
Artifacts Removal

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 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.

Sign in / Sign up

Export Citation Format

Share Document