43 research outputs found
Segmentation of Lesions in Dermoscopy Images Using Saliency Map And Contour Propagation
Melanoma is one of the most dangerous types of skin cancer and causes thousands of deaths worldwide each year. Recently dermoscopic imaging systems have been widely used as a diagnostic tool for melanoma detection. The first step in the automatic analysis of dermoscopy images is the lesion segmentation. In this article, a novel method for skin lesion segmentation that could be applied to a variety of images with different properties and deficiencies is proposed. After a multi-step preprocessing phase (hair removal and illumination correction), a supervised saliency map construction method is used to obtain an initial guess of lesion location. The construction of the saliency map is based on a random forest regressor that takes a vector of regional image features and return a saliency score based on them. This regressor is trained in a multi-level manner based on 2000 training data provided in ISIC2017 melanoma recognition challenge. In addition to obtaining an initial contour of lesion, the output saliency map can be used as a speed function alongside with image gradient to derive the initial contour toward the lesion boundary using a propagation model. The proposed algorithm has been tested on the ISIC2017 training, validation and test datasets, and gained high values for evaluation metrics
A new swarm intelligence information technique for improving information balancedness on the skin lesions segmentation
Methods of image processing can recognize the images of melanoma lesions border in addition to the disease compared to a skilled dermatologist. New swarm intelligence technique depends on meta-heuristic that is industrialized to resolve composite real problems which are problematic to explain by the available deterministic approaches. For an accurate detection of all segmentation and classification of skin lesions, some dealings should be measured which contain, contrast broadening, irregularity quantity, choice of most optimal features, and so into the world. The price essential for the action of progressive disease cases is identical high and the survival percentage is low. Many electronic dermoscopy classifications are advanced depend on the grouping of form, surface and dye features to facilitate premature analysis of malignance. To overcome this problematic, an effective prototypical for accurate boundary detection and arrangement is obtainable. The projected classical recovers the optimization segment of accuracy in its pre-processing stage, applying contrast improvement of lesion area compared to the contextual. In conclusion, optimized features are future fed into of artifical bee colony (ABC) segmentation. Wide-ranging researches have been supported out on four databases named as, ISBI (2016, 2017, 2018) and PH2. Also, the selection technique outclasses and successfully indifferent the dismissed features. The paper shows a different process for lesions optimal segmentation that could be functional to a variation of images with changed possessions and insufficiencies is planned with multistep pre-processing stage
A Review on Skin Disease Classification and Detection Using Deep Learning Techniques
Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
Deep learning based medical image classifiers have shown remarkable prowess
in various application areas like ophthalmology, dermatology, pathology, and
radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD)
systems in real clinical setups is severely limited primarily because their
decision-making process remains largely obscure. This work aims at elucidating
a deep learning based medical image classifier by verifying that the model
learns and utilizes similar disease-related concepts as described and employed
by dermatologists. We used a well-trained and high performing neural network
developed by REasoning for COmplex Data (RECOD) Lab for classification of three
skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and
performed a detailed analysis on its latent space. Two well established and
publicly available skin disease datasets, PH2 and derm7pt, are used for
experimentation. Human understandable concepts are mapped to RECOD image
classification model with the help of Concept Activation Vectors (CAVs),
introducing a novel training and significance testing paradigm for CAVs. Our
results on an independent evaluation set clearly shows that the classifier
learns and encodes human understandable concepts in its latent representation.
Additionally, TCAV scores (Testing with CAVs) suggest that the neural network
indeed makes use of disease-related concepts in the correct way when making
predictions. We anticipate that this work can not only increase confidence of
medical practitioners on CAD but also serve as a stepping stone for further
development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural
Networks (IJCNN) 202
Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition
Deep learning techniques have shown their superior performance in
dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a
challenging task due to the difficulty of incorporating the useful
dermatologist clinical knowledge into the learning process. In this paper, we
propose a novel knowledge-aware deep framework that incorporates some clinical
knowledge into collaborative learning of two important melanoma diagnosis
tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically,
to exploit the knowledge of morphological expressions of the lesion region and
also the periphery region for melanoma identification, a lesion-based pooling
and shape extraction (LPSE) scheme is designed, which transfers the structure
information obtained from skin lesion segmentation into melanoma recognition.
Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma
recognition to skin lesion segmentation, an effective diagnosis guided feature
fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual
learning mechanism that further promotes the inter-task cooperation, and thus
iteratively improves the joint learning capability of the model for both skin
lesion segmentation and melanoma recognition. Experimental results on two
publicly available skin lesion datasets show the effectiveness of the proposed
method for melanoma analysis.Comment: Pattern Recognitio
A survey, review, and future trends of skin lesion segmentation and classification
The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis
MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation
Segmentation is essential for medical image analysis to identify and localize
diseases, monitor morphological changes, and extract discriminative features
for further diagnosis. Skin cancer is one of the most common types of cancer
globally, and its early diagnosis is pivotal for the complete elimination of
malignant tumors from the body. This research develops an Artificial
Intelligence (AI) framework for supervised skin lesion segmentation employing
the deep learning approach. The proposed framework, called MFSNet (Multi-Focus
Segmentation Network), uses differently scaled feature maps for computing the
final segmentation mask using raw input RGB images of skin lesions. In doing
so, initially, the images are preprocessed to remove unwanted artifacts and
noises. The MFSNet employs the Res2Net backbone, a recently proposed
convolutional neural network (CNN), for obtaining deep features used in a
Parallel Partial Decoder (PPD) module to get a global map of the segmentation
mask. In different stages of the network, convolution features and multi-scale
maps are used in two boundary attention (BA) modules and two reverse attention
(RA) modules to generate the final segmentation output. MFSNet, when evaluated
on three publicly available datasets: , ISIC 2017, and HAM10000,
outperforms state-of-the-art methods, justifying the reliability of the
framework. The relevant codes for the proposed approach are accessible at
https://github.com/Rohit-Kundu/MFSNe