78 research outputs found
Detection and Classification Techniques for Skin Lesion Images: A Review
Dermoscopy needs sophisticated and robust systems for successful treatment which would also help reduce the number of biopsies. Computer aided diagnosis of melanoma support clinical decision making which would provide relevant supporting evidence from the prior known cases to the dermatologists and practitioners and also ease the management of clinical data. These systems play an important role of an expert consultant by presenting cases that are not only similar in diagnosis but also similar in appearance and help in early detection and diagnosis of skin diseases. With the advances in technology, new algorithms have also been proposed to develop more efficient CAD systems. This article reviews various techniques that have been proposed for detection and classification of skin lesions
IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation
Skin lesion segmentation plays a crucial role in the computer-aided diagnosis
of melanoma. Deep Learning models have shown promise in accurately segmenting
skin lesions, but their widespread adoption in real-life clinical settings is
hindered by their inherent black-box nature. In domains as critical as
healthcare, interpretability is not merely a feature but a fundamental
requirement for model adoption. This paper proposes IARS SegNet an advanced
segmentation framework built upon the SegNet baseline model. Our approach
incorporates three critical components: Skip connections, residual
convolutions, and a global attention mechanism onto the baseline Segnet
architecture. These elements play a pivotal role in accentuating the
significance of clinically relevant regions, particularly the contours of skin
lesions. The inclusion of skip connections enhances the model's capacity to
learn intricate contour details, while the use of residual convolutions allows
for the construction of a deeper model while preserving essential image
features. The global attention mechanism further contributes by extracting
refined feature maps from each convolutional and deconvolutional block, thereby
elevating the model's interpretability. This enhancement highlights critical
regions, fosters better understanding, and leads to more accurate skin lesion
segmentation for melanoma diagnosis.Comment: Submitted to the journal: Computers in Biology and Medicin
Fast and Accurate Border Detection in Dermoscopy Images Using Statistical Region Merging
Copyright 2007 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.As a result of advances in skin imaging technology and the development of suitable image processing techniques during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, since the accuracy of the subsequent steps crucially depends on it. In this paper, a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the Statistical Region Merging algorithm is presented. The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which a set of dermatologist-determined borders is used as the ground-truth. The proposed method is compared to six state-of-the-art automated methods (optimized histogram thresholding, orientation-sensitive fuzzy c-means, gradient vector flow snakes, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method) and borders determined by a second dermatologist. The results demonstrate that the presented method achieves both fast and accurate border detection in dermoscopy images.http://dx.doi.org/10.1117/12.70907
Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis
Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction
- …