Detection of melanoma skin disease by extracting high level features for skin lesions

Yadav, Vikash; Kaushik, Vandana Dixit

doi:10.1504/ijaip.2018.095493

Cited by 9 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to some major advantages over other descriptors, the HOG descriptor is primarily useful for detecting objects in videos or photos 23 . First, in different artifacts, it can define contour and edge functions that are exceptional.…”

Section: Proposed Workmentioning

confidence: 99%

Skin Lesion Detection Based on a Hybrid Classification and Training Algorithm Using Swarm Intelligence and Neural Networks

Verma,

Sharma

2023

Preprint

View full text Add to dashboard Cite

Skin lesion detection has gained a lot of attention in the last couple of years due to the spread of skin cancer around the world. System-assisted design requires training and classification structure that can only be precise if features are selected appropriately along with improved segmentation of the lesion. This paper introduces a hybrid classification and training algorithm architecture that uses machine learning in all aspects of training and feature selection. The algorithm has been improved by adding novel behavior of Particles and Moth flames to be precise on nature. The proposed algorithm uses a multilayer propagation network for training and classification. The proposed work has been compared with other state-of-the-art algorithms based on quantitative parameters. The proposed work shows a significant improvement in all parameters and aspects.

show abstract

Section: Proposed Workmentioning

confidence: 99%

Skin Lesion Detection Based on a Hybrid Classification and Training Algorithm Using Swarm Intelligence and Neural Networks

Verma,

Sharma

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…• Vikash Yadav et. al [ 7 ], In his paper,author have proposed three high level features for describing the asymmetry of a skin lesion image to help to diagnose it as melanoma or not melanoma. High level features clarify the feature selection process, as it is designed according to human interpretation of the data.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Techniques for the Skin Cancer Detection: A Review

2022

IRJMETS

View full text Add to dashboard Cite

Skin cancer is the most common cancer worldwide, and occurrence rates are continuing to increase. The prediction of the skin cancer is essential to eliminate it on time, so that the life of patient may be saved. The early-stage detection of such disease is helpful for increasing the life span of suffering patient. In addition, it also reduces the cost of treatment. It is better to build an precise expert system, which helps to detect these diseases before leading to terrible conditions. Therefore, many lives can be saved by using these intuitive systems. This paper provides the concise review of medical expert systems and presents the best methods used to recognize the skin cancer.

show abstract

“…In addition to this, training datasets lack sufficient quality in the sense of homogeneity in the acquisition procedure and nonexpected objects present in the image, making it necessary to carry out several preprocessing steps [5] and segment the region of interest [6], [7]. Moreover, another commonly used technique is the extraction of features that are used then to improve the classification rate [8], [9]. The use of specific features extracted from the melanoma images was widely used to develop classification models [10]- [12], although the main inconvenience of these approaches is the requirement of specific expertise to extract the adequate features and the high quantity of time necessary to select the most appropriate.…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Classification by Ensembles of Deep Convolutional Networks and Regularly Spaced Shifting

et al. 2021

View full text Add to dashboard Cite

under grant RTI2018-094645-B-I00, project name Automated detection with low-cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by deep learning in low-cost video surveillance intelligent systems. All of them include funds from the European Regional Development Fund (ERDF). It is also partially supported by the University of Malaga (Spain) under grants B1-2019_02, project name Self-Organizing Neural Systems for Non-Stationary Environments, and B1-2019_01, project name Anomaly detection on roads by moving cameras. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs. The authors acknowledge the funding from the Universidad de Málaga. Funding for open access charge: Universidad de Málaga / CBUA.

show abstract

Detection of melanoma skin disease by extracting high level features for skin lesions

Cited by 9 publications

References 0 publications

Skin Lesion Detection Based on a Hybrid Classification and Training Algorithm Using Swarm Intelligence and Neural Networks

Skin Lesion Detection Based on a Hybrid Classification and Training Algorithm Using Swarm Intelligence and Neural Networks

Machine Learning Techniques for the Skin Cancer Detection: A Review

Skin Lesion Classification by Ensembles of Deep Convolutional Networks and Regularly Spaced Shifting

Contact Info

Product

Resources

About