Malignant Melanoma Detection Using Multi Layer Perceptron with Optimized Network Parameter Selection by PSO

2018 ◽  
pp. 101-109
Author(s):  
Soumen Mukherjee ◽  
Arunabha Adhikari ◽  
Madhusudan Roy
Keyword(s):  
Malignant Melanoma ◽  
Parameter Selection ◽  
Multi Layer Perceptron ◽  
Network Parameter ◽  
Melanoma Detection

scholarly journals hybSVM: Bacterial colony optimization algorithm based SVM for malignant melanoma detection

2021 ◽  
Author(s):  
Sümeyya İlkin ◽  
Tuğrul Hakan Gençtürk ◽  
Fidan Kaya Gülağız ◽  
Hikmetcan Özcan ◽  
Mehmet Ali Altuncu ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Optimization Algorithm ◽  
Bacterial Colony ◽  
Melanoma Detection

Human malignant melanoma: detection of BRAF- and c-kit–activating mutations by high-resolution amplicon melting analysis

2005 ◽  
Vol 36 (5) ◽  
pp. 486-493 ◽  
Author(s):  
Carlynn Willmore-Payne ◽  
Joseph A. Holden ◽  
Sheryl Tripp ◽  
Lester J. Layfield
Keyword(s):  
High Resolution ◽  
Malignant Melanoma ◽  
Human Malignant Melanoma ◽  
Activating Mutations ◽  
Melanoma Detection ◽  
Melting Analysis

scholarly journals Techniques for Malignant Melanoma Diagnosis: A Systematic Literature Review

2020 ◽  
Vol 9 (3) ◽  
pp. 80-86
Keyword(s):  
Malignant Melanoma ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Current Status ◽  
Support Vector ◽  
Melanoma Detection ◽  
Melanoma Diagnosis ◽  
Research Questions ◽  
Detection And Diagnosis ◽  
Images Processing

Malignant melanoma is the deadliest type of skin cancer. If melanoma detection and diagnosis is performed in its early stages, the probabilities of recovery and survival are higher. Dermoscopy is a manual method which is applied by doctors to diagnose this disease, but it strongly depends on the experience of the specialist who performs this skin assessment. Although, many proposals have been made for automated detection and diagnosis of malignant melanoma based on images processing, there are still improvement opportunities for melanoma diagnosis. This paper aims to identify the current status of the latest researches related to techniques for malignant melanoma diagnosis based on images analysis, considering the three research questions that have been elaborated for the systematic literature review: Q1) Which are the latest methods for malignant melanoma detection? Q2) Which systems for malignant melanoma diagnosis have been implemented in the last 5 years? And Q3) Which CAD systems for malignant melanoma detection have been developed? Furthermore, a cross-analysis of the outcome was performed. The results propose the implementation of systems using Inception V3 and the classifier Support Vector Machine, which achieved high accuracies in malignant melanoma diagnosis based on images processing.

scholarly journals A Methodological Approach for Early Melanoma Detection Using Smartphone Captured Images

2019 ◽  
pp. 503-510
Author(s):  
Haritha U ◽  
Muhammad Shameem
Keyword(s):  
Malignant Melanoma ◽  
Methodological Approach ◽  
Selection Criterion ◽  
Image Features ◽  
Motion Blur ◽  
Color Variation ◽  
Correct Result ◽  
Melanoma Detection ◽  
Blurred Image ◽  
New Feature

Researches on applications of mobile devices bring wide variety of uses in healthcare. One such work focus on detection of malignant melanoma using mobile image analysis. Dermoscopy is one of a current use, but need a special expertise for the detection of cancer melanoma. The image taken using smartphone is used for this purpose. It mainly focus on localization of the skin lesion by combining fast skin detection and fusion of two fast segmentation results. This also introduces some set of image features and to capture color variation and border irregularity which are useful for smartphone-captured images. It propose a new feature selection criterion to select a small set of good features used in the final lightweight system. The method introduces a new module for the detection of distorted images such as motion blur and alert users in such situations. The blurred image undergo deblurring to detect the correct result. The result of this application will identify whether the image is malignant melanoma or benign with their intensity value from smartphone captured images used.

scholarly journals Deep Learning Classifier with Patient’s Metadata of Dermoscopic Images in Malignant Melanoma Detection

2021 ◽  
Vol Volume 14 ◽  
pp. 877-885
Author(s):  
Dina Nur Anggraini Ningrum ◽  
Sheng-Po Yuan ◽  
Woon-Man Kung ◽  
Chieh-Chen Wu ◽  
I-Shiang Tzeng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Malignant Melanoma ◽  
Melanoma Detection ◽  
Learning Classifier
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