Melanoma Detection and Classification using Computerized Analysis of Dermoscopic Systems: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 794-822
Author(s):  
Muhammad Nasir ◽  
Muhammad Attique Khan ◽  
Muhammad Sharif ◽  
Muhammad Younus Javed ◽  
Tanzila Saba ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Performance Measure ◽  
Diagnostic Methods ◽  
High Survival ◽  
Features Selection ◽  
Melanoma Detection ◽  
And Performance ◽  
Contrast Stretching ◽  
Improved Accuracy

Malignant melanoma is considered as one of the most deadly cancers, which has broadly increased worldwide since the last decade. In 2018, around 91,270 cases of melanoma were reported and 9,320 people died in the US. However, diagnosis at the initial stage indicates a high survival rate. The conventional diagnostic methods are expensive, inconvenient and subject to the dermatologist’s expertise as well as a highly equipped environment. Recent achievements in computerized based systems are highly promising with improved accuracy and efficiency. Several measures such as irregularity, contrast stretching, change in origin, feature extraction and feature selection are considered for accurate melanoma detection and classification. Typically, digital dermoscopy comprises four fundamental image processing steps including preprocessing, segmentation, feature extraction and reduction, and lesion classification. Our survey is compared with the existing surveys in terms of preprocessing techniques (hair removal, contrast stretching) and their challenges, lesion segmentation methods, feature extraction methods with their challenges, features selection techniques, datasets for the validation of the digital system, classification methods and performance measure. Also, a brief summary of each step is presented in the tables. The challenges for each step are also described in detail, which clearly indicate why the digital systems are not performing well. Future directions are also given in this survey.

scholarly journals Object Detection and Tracking using Multiple Features Extraction

2021 ◽  
Vol 10 (10) ◽  
pp. 75-79
Author(s):  
Bhavya Rudraiah* ◽  
◽  
Dr. Geetha K. S. ◽  
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
Extraction Methods ◽  
Vital Role ◽  
Histogram Of Oriented Gradients ◽  
Multiple Objects ◽  
Multiple Features ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
And Performance

In most of the video analysis applications, object detection and tracking play vital role. Most of detection and tracking algorithms fail to predict multiple objects with varying orientation. In this paper, the goal is to identify and track multiple objects using different feature extraction methods like Locality Sensitive Histogram, Histogram of Oriented Gradients and Edges. These features are subjected to train classifier that can detect the object of different orientations. Experimental results and performance evaluation depicts the proposed method which uses LSH performs well with an increased accuracy of 98%. This method can precisely track the object and can be utilized to track under different scale and pose variations.

A Review on Feature Extraction Methods for Image Analysis

2020 ◽  
Author(s):  
Vricha Chavan ◽  
​Jimit Shah ◽  
Mrugank Vora ◽  
Mrudula Vora ◽  
Shubhashini Verma
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Extraction Methods

scholarly journals Feature Extraction for Finger-Vein-Based Identity Recognition

2021 ◽  
Vol 7 (5) ◽  
pp. 89
Author(s):  
George K. Sidiropoulos ◽  
Polixeni Kiratsa ◽  
Petros Chatzipetrou ◽  
George A. Papakostas
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Methods ◽  
Identity Recognition ◽  
Learning Framework ◽  
Finger Vein ◽  
The Past ◽  
Biometric Systems ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

Does What We See Shape History? Examining Workload History as a Function of Performance and Ambient/Focal Visual Attention

2021 ◽  
Vol 18 (2) ◽  
pp. 1-17
Author(s):  
Shannon P. Devlin ◽  
Jennifer K. Byham ◽  
Sara Lu Riggs
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Performance Measure ◽  
Task Demands ◽  
Unexpected Finding ◽  
Impact Performance ◽  
History Effects ◽  
And Performance ◽  
Workload History ◽  
Over Time

Changes in task demands can have delayed adverse impacts on performance. This phenomenon, known as the workload history effect, is especially of concern in dynamic work domains where operators manage fluctuating task demands. The existing workload history literature does not depict a consistent picture regarding how these effects manifest, prompting research to consider measures that are informative on the operator's process. One promising measure is visual attention patterns, due to its informativeness on various cognitive processes. To explore its ability to explain workload history effects, participants completed a task in an unmanned aerial vehicle command and control testbed where workload transitioned gradually and suddenly. The participants’ performance and visual attention patterns were studied over time to identify workload history effects. The eye-tracking analysis consisted of using a recently developed eye-tracking metric called coefficient K , as it indicates whether visual attention is more focal or ambient. The performance results found workload history effects, but it depended on the workload level, time elapsed, and performance measure. The eye-tracking analysis suggested performance suffered when focal attention was deployed during low workload, which was an unexpected finding. When synthesizing these results, they suggest unexpected visual attention patterns can impact performance immediately over time. Further research is needed; however, this work shows the value of including a real-time visual attention measure, such as coefficient K , as a means to understand how the operator manages varying task demands in complex work environments.

scholarly journals Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System

2021 ◽  
Vol 11 (15) ◽  
pp. 6748
Author(s):  
Hsun-Ping Hsieh ◽  
Fandel Lin ◽  
Jiawei Jiang ◽  
Tzu-Ying Kuo ◽  
Yu-En Chang
Keyword(s):  
New York ◽  
Feature Extraction ◽  
Real World ◽  
Extraction Methods ◽  
Real World Data ◽  
Urban Dynamics ◽  
Bike Sharing ◽  
The Government ◽  
Short Time

Research on flourishing public bike-sharing systems has been widely discussed in recent years. In these studies, many existing works focus on accurately predicting individual stations in a short time. This work, therefore, aims to predict long-term bike rental/drop-off demands at given bike station locations in the expansion areas. The real-world bike stations are mainly built-in batches for expansion areas. To address the problem, we propose LDA (Long-Term Demand Advisor), a framework to estimate the long-term characteristics of newly established stations. In LDA, several engineering strategies are proposed to extract discriminative and representative features for long-term demands. Moreover, for original and newly established stations, we propose several feature extraction methods and an algorithm to model the correlations between urban dynamics and long-term demands. Our work is the first to address the long-term demand of new stations, providing the government with a tool to pre-evaluate the bike flow of new stations before deployment; this can avoid wasting resources such as personnel expense or budget. We evaluate real-world data from New York City’s bike-sharing system, and show that our LDA framework outperforms baseline approaches.

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