Extraction of Skin Lesions from Non-Dermoscopic Images Using Deep Learning

TL;DR

This paper presents a deep learning-based method for accurately segmenting skin lesions from non-dermoscopic images, addressing challenges like illumination and contrast variations to improve early melanoma detection.

Contribution

It introduces a novel CNN-based approach with effective patch selection and post-processing for improved lesion segmentation in standard camera images.

Findings

Outperforms existing state-of-the-art segmentation algorithms.

Achieves high accuracy in lesion detection despite illumination and contrast issues.

Demonstrates robustness across diverse skin lesion images.

Abstract

Melanoma is amongst most aggressive types of cancer. However, it is highly curable if detected in its early stages. Prescreening of suspicious moles and lesions for malignancy is of great importance. Detection can be done by images captured by standard cameras, which are more preferable due to low cost and availability. One important step in computerized evaluation of skin lesions is accurate detection of lesion region, i.e. segmentation of an image into two regions as lesion and normal skin. Accurate segmentation can be challenging due to burdens such as illumination variation and low contrast between lesion and healthy skin. In this paper, a method based on deep neural networks is proposed for accurate extraction of a lesion region. The input image is preprocessed and then its patches are fed to a convolutional neural network (CNN). Local texture and global structure of the patches are processed in order to assign pixels to lesion or normal classes. A method for effective selection of training patches is used for more accurate detection of a lesion border. The output segmentation mask is refined by some post processing operations. The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.