Diagnosing skin cancer via C‐means segmentation with enhanced fuzzy optimization-Reference-Cited by-同舟云学术

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Diagnosing skin cancer via C‐means segmentation with enhanced fuzzy optimization

Published:2021-04-08 Issue: Volume: Page:
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET image process

Author:

Durgarao Nagayalanka¹,Sudhavani Ghanta²

Affiliation:

1. Department of ECE Acharya Nagarjuna University Guntur Andhra Pradesh India

2. Department of ECE R.V.R & J.C College of Engineering Guntur Andhra Pradesh India

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12194

Reference35 articles.

1. Endogenous and Exogenous Fluorescence Skin Cancer Diagnostics for Clinical Applications

2. Automated Detection and Segmentation of Vascular Structures of Skin Lesions Seen in Dermoscopy, With an Application to Basal Cell Carcinoma Classification

3. Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention

4. Microwave Reflectometry as a Novel Diagnostic Tool for Detection of Skin Cancers

5. Accurate Time-Domain Modeling of Reconfigurable Antenna Sensors for Non-Invasive Melanoma Skin Cancer Detection

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1. Performance evaluation of E-VGG19 model: Enhancing real-time skin cancer detection and classification;Heliyon;2024-05

2. An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks;Diagnostics;2024-03-17

3. DOTHE based image enhancement and segmentation using U-Net for effective prediction of human skin cancer;Multimedia Tools and Applications;2024-02-12

4. A review of various techniques used for classification of dermatoscopic images using machine learning or deep learning classifiers;2023 Second International Conference on Informatics (ICI);2023-11-23

5. Ti-FCNet: Triple fused convolutional neural network-based automated skin lesion classification;Multimedia Tools and Applications;2023-09-22

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