Skin lesion segmentation from dermoscopic images by using Mask R-CNN, Retina-Deeplab, and graph-based methods

Dermo-Seg: ResNet-UNet Architecture and Hybrid Loss Function for Detection of Differential Patterns to Diagnose Pigmented Skin Lesions

Convolutional neural network (CNN) models have been extensively applied to skin lesions segmentation due to their information discrimination capabilities. However, CNNs' struggle to capture the connection between long-range contexts when extracting deep semantic features from lesion images, resulting in a semantic gap that causes segmentation distortion in skin lesions. Therefore, detecting the presence of differential structures such as pigment networks, globules, streaks, negative networks, and milia-like cysts becomes difficult. To resolve these issues, we have proposed an approach based on semantic-based segmentation (Dermo-Seg) to detect differential structures of lesions using a UNet model with a transfer-learning-based ResNet-50 architecture and a hybrid loss function. The Dermo-Seg model uses ResNet-50 backbone architecture as an encoder in the UNet model. We have applied a combination of focal Tversky loss and IOU loss functions to handle the dataset's highly imbalanced class ratio. The obtained results prove that the intended model performs well compared to the existing models. The dataset was acquired from various sources, such as ISIC18, ISBI17, and HAM10000, to evaluate the Dermo-Seg model. We have dealt with the data imbalance present within each class at the pixel level using our hybrid loss function. The proposed model achieves a mean IOU score of 0.53 for streaks, 0.67 for pigment networks, 0.66 for globules, 0.58 for negative networks, and 0.53 for milia-like-cysts. Overall, the Dermo-Seg model is efficient in detecting different skin lesion structures and achieved 96.4% on the IOU index. Our Dermo-Seg system improves the IOU index compared to the most recent network.

A survey on deep learning for skin lesion segmentation

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online³.

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.

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

The skin is the human body's largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research.

A Survey on Computer-Aided Intelligent Methods to Identify and Classify Skin Cancer

Melanoma is one of the skin cancer types that is more dangerous to human society. It easily spreads to other parts of the human body. An early diagnosis is necessary for a higher survival rate. Computer-aided diagnosis (CAD) is suitable for providing precise findings before the critical stage. The computer-aided diagnostic process includes preprocessing, segmentation, feature extraction, and classification. This study discusses the advantages and disadvantages of various computer-aided algorithms. It also discusses the current approaches, problems, and various types of datasets for skin images. Information about possible future works is also highlighted in this paper. The inferences derived from this survey will be useful for researchers carrying out research in skin cancer image analysis.

A novel hybrid meta-heuristic contrast stretching technique for improved skin lesion segmentation

The high precedence of epidemiological examination of skin lesions necessitated the well-performing efficient classification and segmentation models. In the past two decades, various algorithms, especially machine/deep learning-based methods, replicated the classical visual examination to accomplish the above-mentioned tasks. These automated streams of models demand evident lesions with less background and noise affecting the region of interest. However, even after the proposal of these advanced techniques, there are gaps in achieving the efficacy of matter. Recently, many preprocessors proposed to enhance the contrast of lesions, which further aided the skin lesion segmentation and classification tasks. Metaheuristics are the methods used to support the search space optimisation problems. We propose a novel Hybrid Metaheuristic Differential Evolution-Bat Algorithm (DE-BA), which estimates parameters used in the brightness preserving contrast stretching transformation function. For extensive experimentation we tested our proposed algorithm on various publicly available databases like ISIC 2016, 2017, 2018 and PH², and validated the proposed model with some state-of-the-art already existing segmentation models. The tabular and visual comparison of the results concluded that DE-BA as a preprocessor positively enhances the segmentation results.

Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering

Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there may be a variation of contrast near the lesion boundaries, lesions may have different sizes and shapes, etc. Therefore, lesion extraction from dermoscopic images is a fundamental step for automated melanoma identification. In this article, a watershed transform based on the fast fuzzy c-means (FCM) clustering algorithm is proposed for the extraction of melanocytic skin lesion from dermoscopic images. Initially, the proposed method removes the artifacts from the dermoscopic images and enhances the texture regions. Further, it is filtered using a Gaussian filter and a local variance filter to enhance the lesion boundary regions. Later, the watershed transform based on MMLVR (multiscale morphological local variance reconstruction) is introduced to acquire the superpixels of the image with accurate boundary regions. Finally, the fast FCM clustering technique is implemented in the superpixels of the image to attain the final lesion extraction result. The proposed method is tested in the three publicly available skin lesion image datasets, i.e., ISIC 2016, ISIC 2017 and ISIC 2018. Experimental evaluation shows that the proposed method achieves a good result.