Modified Topological Image Preprocessing for Skin Lesion Classifications

08/13/2023
by   Hong Cheng, et al.
0

This paper proposes a modified Topological Data Analysis model for skin images preprocessing and enhancements. The skin lesion dataset HAM10000 used with the intention of identifying the important objects in relevant regions of the images. In order to evaluate both the original dataset and the preprocessed dataset, Deep Convolutional Neural Network and Vision Transformer models were utilized to train both models. After training, the experimental results demonstrate that the images preprocessed using the Modified Topological Data Analysis consistently perform better.

READ FULL TEXT

page 5

page 7

research
07/24/2018

Skin disease identification from dermoscopy images using deep convolutional neural network

In this paper, a deep neural network based ensemble method is experiment...
research
02/06/2022

Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification

Convolutional Neural Networks have demonstrated human-level performance ...
research
03/13/2020

Advanced Deep Learning Methodologies for Skin Cancer Classification in Prodromal Stages

Technology-assisted platforms provide reliable solutions in almost every...
research
05/13/2019

TopoResNet: A hybrid deep learning architecture and its application to skin lesion classification

Skin cancer is one of the most common cancers in the United States. As t...
research
05/18/2020

Measles Rash Image Detection Using Deep Convolutional Neural Network

Measles is extremely contagious and is one of the leading causes of vacc...
research
03/15/2017

Skin lesion segmentation based on preprocessing, thresholding and neural networks

This abstract describes the segmentation system used to participate in t...
research
11/13/2019

A Smartphone-Based Skin Disease Classification Using MobileNet CNN

The MobileNet model was used by applying transfer learning on the 7 skin...

Please sign up or login with your details

Forgot password? Click here to reset