ELM-UNet: An efficient and lightweight Vision Mamba UNet for skin lesion segmentation

Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.

The segmentation of a skin lesion is regarded as very challenging because of the low contrast between the lesion and the surrounding skin, the existence of various artifacts, and different imaging acquisition conditions. The purpose of this study is to segment melanocytic skin lesions in dermoscopic and standard images by using a hybrid model combining a new hierarchical K-means and level set approach, called HK-LS. Although the level set method is usually sensitive to initial estimation, it is widely used in biomedical image segmentation because it can segment more complex images and does not require a large number of manually labelled images. The preprocessing step is used for the proposed model to be less sensitive to intensity inhomogeneity. The proposed method was evaluated on medical skin images from two publicly available datasets including the PH2 database and the Dermofit database. All skin lesions were segmented with high accuracies (>94%) and Dice coefficients (>0.91) of the ground truth on two databases. The quantitative experimental results reveal that the proposed method yielded significantly better results compared to other traditional level set models and has a certain advantage over the segmentation results of U-net in standard images. The proposed method had high clinical applicability for the segmentation of melanocytic skin lesions in dermoscopic and standard images.

Segmentation of skin lesions is a crucial task in detecting and diagnosing melanoma cancer. Incidence of melanoma skin cancer which is the most deadly form of skin cancer has been on steady increase. Early detection of the melanoma cancer is necessary to improve the survival rate of the patients. Segmentation is an important task in analysing skin lesion images. Skin lesion segmentation has come with some challenges such as low contrast and fine grained nature of skin lesions. This has necessitated the need for automated analysis and segmentation of skin lesions using state-of-the-arts techniques. In this paper, a deep learning model has been adapted for the segmentation of skin lesions. This work demonstrates the segmentation of skin lesions using fully convolutional networks (FCNs) that train skin lesion images from end-to-end using only the images pixels and disease ground truth labels as inputs. The fully convolutional network adapted is based on U-Net architecture. The model is enhanced by employing multi-stage segmentation approach with batch normalisation and data augmentation. Performance metrics such as dice coefficient, accuracy, sensitivity and specificity were used for evaluating the performance of the model. Experimental results show that the proposed model achieved better performance compared with the other state-of-the arts methods for skin lesion image segmentation with a dice coefficient of \(90\%\) and sensitivity of \(96\%\).

Segmentation of melanocytic skin lesions using feature learning and dictionaries

Skin cancer is the irregular growth of skin cells, which is most often termed as cancer, developed by exposure of ultraviolet rays from sun. In this research paper, deep learning enabled hybrid optimization is followed for skin cancer detection and lesion segmentation. Two optimization algorithms are followed for skin lesion segmentation and cancer detection. Here, pre-processing is done by anisotropic diffusion followed by skin lesion segmentation. Here, Multi-Scale Residual Fusion Network (MSRFNet) is utilized for skin lesion segmentation, which is trained by proposed Average Subtraction Student Psychology Based Optimization (ASSPBO). After skin lesion segmentation, necessary features are extracted, followed by skin cancer detection. Skin cancer is detected by Deep Residual Network (DRN) trained by proposed Fractional ASSPBO (FrASSPBO). Moreover, performance of proposed FrASSPBO-DRN is analysed by three performance metrics like testing accuracy, True Positive Rate (TPR), and False Positive Rate (FPR) with values of 93.4%, 94%, and 8.2%.

BackgroundSkin diseases are severe diseases. Identification of these severe diseases depends upon the abstraction of atypical skin regions. The segmentation of these skin diseases is essential to rheumatologists in risk impost and for valuable and vital decision‐making. Skin lesion segmentation from images is a crucial step toward achieving this goal—timely exposure of malignancy in psoriasis expressively intensifies the persistence ratio. Defies occur when people presume skin diseases they have without accurately and precisely incepted. However, analyzing malignancy at runtime is a big challenge due to the truncated distinction of the visual similarity between malignance and non‐malignance lesions. However, images' different shapes, contrast, and vibrations make skin lesion segmentation challenging. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation.Materials and methodsThis paper introduces a skin lesions segmentation model that integrates two intelligent methodologies: Bayesian inference and edge intelligence. In the segmentation model, we deal with edge intelligence to utilize the texture features for the segmentation of skin lesions. In contrast, Bayesian inference enhances skin lesion segmentation's accuracy and efficiency.ResultsWe analyze our work along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions from seminal works and a systematic viewpoint and examine how these dimensions have influenced current trends.ConclusionWe summarize our work with previously used techniques in a comprehensive table to facilitate comparisons. Our experimental results show that Bayesian‐Edge networks can boost the diagnostic performance of skin lesions by up to 87.80% without incurring additional parameters of heavy computation.

Segmentation which is identification of regions of interest (ROIs) in medical images is a very important step for image analysis in computer-aided diagnosis systems. Accurate segmentation of skin lesions images plays a vital role in efficient diagnosis of melanoma skin cancer. Diagnosis of melanoma cancer through the segmentation of skin lesions is a challenging task due to possible presence of noise and artefacts such as hairs, air or oil bubbles on the skin lesion images. Skin lesions images are also sometimes characterized with weak edges, irregular and fuzzy borders, marks, dark corners, skin lines and blood vessels on skin lesions. Recently, segmentation methods based on Fully Convolutional Encoder-Decoder Architecture (FCEDA) have achieved great success in medical images. This work presents automatic skin lesion segmentation method that is based on Fully Convolutional Encoder-Decoder Architecture. Two types of FCEDA namely U-Net and SegNet architectures, have been examined and utilized for segmentation of skin lesion images. The performance analysis of the two architectures have been conducted. Evaluation and comparison of these two architectures were also carried out. This work finds out and proposes possible improvements of these methods on the segmentation of skin lesions. It is also a systematic comparison of U-Net and SegNet models on the segmentation of skin lesion images. The paper discovers how deep learning methods can be explored using a supervised approach to get accurate results with less complexity possible. The models were evaluated on skin lesion challenge dataset in ISIC 2018 dermoscopic images archives.

Segmentation is the pre-requisite process in most of the computer aided diagnosis systems for medical imaging. Presence of different artifacts makes segmentation of skin lesion very difficult. Abnormal growth of artifacts can appear as false positives and can degrade the performance of the diagnosis systems. It can be avoided only when false structures are removed while extracting the lesion. To address this issue, this paper proposes deep leaning for skin lesion segmentation. Within this framework, automated skin lesion segmentation is proposed which achieves high accuracy segmentation of skin lesion. Our proposed architecture is 31 layers deep with same filter size. The validity of the proposed techniques is tested on two publically available databases of PH2 and ISIC 2017. Experimental results show the efficiency of the proposed approaches. The proposed method gives Dice Coefficient of 92.3% for PH2 Dataset while Dice Coefficient of 85.5% for ISIC 2017 Dataset.

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced-SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.

During a dermoscopy examination, accurate and automatic skin lesion detection and segmentation can assist medical experts in resecting problematic areas and decrease the risk of deaths due to skin cancer. In order to develop fully automated deep learning model for skin lesion segmentation, the authors design a model Attention Res-UNet by incorporating residual connections, squeeze and excite units, atrous spatial pyramid pooling, and attention gates in basic UNet architecture. This model uses focal tversky loss function to achieve better trade off among recall and precision when training on smaller size lesions while improving the overall outcome of the proposed model. The results of experiments have demonstrated that this design, when evaluated on publicly available ISIC 2018 skin lesion segmentation dataset, outperforms the existing standard methods with a Dice score of 89.14% and IoU of 81.16%; and achieves better trade off among precision and recall. The authors have also performed statistical test of this model with other standard methods and evaluated that this model is statistically significant.

With the ever growing occurrences of skin cancer and limited healthcare settings, a reliable computer assisted diagnostic system is needed to assist the dermatologists for lesion diagnosis. Skin lesion segmentation on dermo- scopic images can be an efficient tool to determine the differences between benign and malignant skin lesions. The dermoscopic images in the public skin lesion datasets are collected from various sources around the world. The color of lesions in dermoscopic images can be strongly dependent on the light source. In this work, we provide a new insight on the effect of color constancy algorithms on skin lesion segmentation with deep learning algorithm. We pre-process the ISIC Challenge Segmentation 2017 dataset using different color constancy algorithms and study the effect on a popular semantic segmentation algorithm, i.e. Fully Convolutional Networks. We evaluate the results with two evaluation metrics, i.e. Dice Similarity Coefficient and Jaccard Similarity Index. Overall, our experiments showed improvements in semantic segmentation of skin lesions when pre-processed with color constancy algorithms. Further, we investigate the effect of these algorithms on different types of lesions (Naevi, Melanoma and Seborrhoeic Keratosis). We found pre-processing with color constancy algorithms improved the segmentation results on Naevi and Seborrhoeic Keratosis, but not Melanoma. Future work will seek to investigate an adaptive color constancy algorithm that could improve the segmentation results.

Understanding skin color bias in deep learning-based skin lesion segmentation

Segmentation of skin lesion using superpixel guided generative adversarial network with dual-stream patch-based discriminators

In dermatology, accurate classification and segmentation of skin lesions remain important challenges due to variations in lesion appearance, low contrast in images, and overlapping features across disease types. Various traditional models pose some major difficulties in simultaneously capturing fine-grained local features and global environmental dependencies. Hence, this paper proposes a novel model that integrates a Residual Attention DeepLabV3 + Network for the segmentation, and a dual attention conformer for the classification, employing deep learning approaches to enhance both segmentation and classification performance of skin lesions. Publicly available skin disease image datasets are employed, with the pre-processing steps including Image rescaling and normalization, Histogram equalization, Gaussian blurring, edge detection, noise remover, and Contrast Limited Adaptive Histogram Equalization. To evaluate the performance of the model, the dataset is split using the holdout method, with a ratio of 80:10:10 for the training, testing, and validation phases. Based on the labeled ISIC dermatology image dataset, the simulation results show the excellent performance of the proposed model with 99.18%, 99.06%, and 98.92% accuracy, precision and recall. Overall, the proposed model achieves superior results in skin lesion classification and segmentation and offers promising potential for integration into medical diagnostic systems.

An automatic skin lesion segmentation system with hybrid FCN-ResAlexNet