Modified Topological Image Preprocessing for Skin Lesion Classifications

TL;DR

This paper introduces a modified topological data analysis method for preprocessing skin lesion images, improving the performance of CNN and Vision Transformer models in skin lesion classification tasks.

Contribution

The paper presents a novel modification to topological data analysis for image preprocessing, enhancing model accuracy in skin lesion classification.

Findings

Preprocessed images with the modified TDA outperform original images in classification accuracy.

Deep learning models trained on preprocessed data show improved performance.

Modified TDA preprocessing consistently enhances model results across experiments.

Abstract

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.