Machine learning based skin lesion segmentation method with novel borders and hair removal techniques

Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.

Segmentation of skin lesion images using discrete wavelet transform

Automatic lesion segmentation in dermoscopy images is an essential step for computer-aided diagnosis of melanoma. The dermoscopy images exhibits rotational and reflectional symmetry, however, this geometric property has not been encoded in the state-of-the-art convolutional neural networks based skin lesion segmentation methods. In this paper, we present a deeply supervised rotation equivariant network for skin lesion segmentation by extending the recent group rotation equivariant network~\cite{cohen2016group}. Specifically, we propose the G-upsampling and G-projection operations to adapt the rotation equivariant classification network for our skin lesion segmentation problem. To further increase the performance, we integrate the deep supervision scheme into our proposed rotation equivariant segmentation architecture. The whole framework is equivariant to input transformations, including rotation and reflection, which improves the network efficiency and thus contributes to the segmentation performance. We extensively evaluate our method on the ISIC 2017 skin lesion challenge dataset. The experimental results show that our rotation equivariant networks consistently excel the regular counterparts with the same model complexity under different experimental settings. Our best model achieves 77.23\%(JA) on the test dataset, outperforming the state-of-the-art challenging methods and further demonstrating the effectiveness of our proposed deeply supervised rotation equivariant segmentation network. Our best model also outperforms the state-of-the-art challenging methods, which further demonstrate the effectiveness of our proposed deeply supervised rotation equivariant segmentation network.

Skin lesion segmentation plays an important role in automatic skin cancer diagnosis. One of the most dangerous types of this cancer is melanoma which requires an early and accurate detection. However, automatic melanoma segmentation on dermoscopic images is a challenging task since images are corrupted by noise like hairs, air bubbles, blood vessel… and with fuzzy boundaries. This paper presents a framework based on deep fully convolutional neural networks to automatically segment skin lesions in dermoscopic images. Particularly, the paper proposes a fully convolutional network (FCN) framework that is based on modification of the SegNet architecture. In particular, we propose to reduce the downsampling and upsampling layers in the original SegNet model, that reduces total learned parameters compared to the original SegNet model. The proposed approach is applied to segment images from ISIC 2017 dataset. Experimental results show the desired performances of the proposed approach in terms of metrics of Dice coefficient and Jaccard indexes.

The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms.

Malignant melanoma is one of the most aggressive forms of skin cancer which must be necessary to be diagnosed at the initial stage for effective treatment. Melanoma affects the patient life even it can become a reason of death if its diagnosis is not accomplished on time. Through a rough pigment network and some suspicious signs can be helpful for diagnosis the melanoma from dermoscopic images. According to the clinical studies, for dermatologists, it is quite difficult to identify these signs at the initial stage of melanoma. So, it is important to propose an automated system which can efficiently be identified and differentiate between benign and malignant melanoma. The main focus of this research article is to improve the skin lesion segmentation from low contrast and under/ over segmented dermoscopic images through fusing the region based active contour method with JSEG method. The proposed fused segmentation technique gain 95.3% accuracy and through our proposed feature vector the Gaussian classifier achieved the promising results as sensitivity 97.7%, specificity 96.7%, and accuracy 97.5% with handling the special dermoscopic image cases which are comparatively much better than numerous exiting techniques.

Skin lesion segmentation in dermoscopic images is a challenging task in the domain of medical images analysis because of the irregular and blurring edges of the lesion and the presence of various artifacts. Inspired by the successful applications of the generative adversarial network (GAN), we propose a new neural network for the segmentation of skin lesion. Different from the traditional adversarial network, the segmentation network uses encoder-decoder with the dense-residual block which enables the network to be trained more efficiently. It can also establish a direct relationship between adjacent pixels to improve segmentation accuracy. A multi-scale objective loss function is used to utilize deep supervision in multiple layers of the critic network. End point error and Jaccard distance are combined as the content loss function. It can solve the problem of boundary ambiguity and solve the lesion-background imbalance in pixel-level classification for skin lesion segmentation. We finally use a joint loss function including a multi-scale objective loss function, end point error, and Jaccard distance content loss function. The experiment results show that our algorithm is superior to other state-of-the-art algorithms on the ISBI2017 and PH2 datasets.

AFLN-DGCL: Adaptive Feature Learning Network with Difficulty-Guided Curriculum Learning for skin lesion segmentation

About 2 to 3 million new cases of skin cancer are identified every year, making it one of the most common forms of cancer. Skin lesions are the result of anomalous cell reproduction on the skin, and examining them manually is a tough, instinctive, and arduous task. Computer-Aided Diagnosis (CAD) technologies allow practitioners to become more proficient in their investigative abilities while at the same time reducing the amount of time necessary to carry out an appropriate diagnosis. However, the restricted accessibility of several advanced computer-aided diagnoses (CAD) systems that are easier to use has resulted in a significant amount of uncertainty over the rapid, non-invasive, and reliable detection of diseases. According to the existing literature, not nearly enough effort has been put into creating CAD systems for skin lesions. With a primary focus on pre-processing, segmentation, and classification of skin lesions in dermoscopic images, the goal of this article is to give a complete and critical bibliography addressing the published literature on this topic. The purpose of this paper is to provide detailed yet necessary references. The main goal is to give researchers who are new to this field and don't know enough about it to understand all of its technical details an overview. In addition, it identifies some of the research challenges that can be identified in the literature and need to be addressed by future researchers to investigate innovative pathways in this area.

Skin cancer is the most predominat cancer in the world. Detection of skin cancer in earrly stages helps to decrease the fatality rate. Image processing and AI plays vital role in early detection of skin cancer. Dermoscopy images are used by doctors to detect skin cancer.In this paper, shape based features such as eccentricity, perimeter, convex area, orientation,area, ratio of major axis to minor acis length, eqvidiameter have been extracted from segmented skin lesion lesions. Subsequntly these features are give different classifiers such as SVM,KNN,ensemble learning. The efficacy of the proposed method was evaluated on 200 segmented skin lesions of common nevus, atypical nevus and melanoma with different class combinations. The proposed method achieved accuracy of 90% accuracy (acc) and 93% sensitivity (sen) for classifying segmented skin lesions into commatypical nevi and malignant melanoma. This approach helps dermatologists in screening skin lesions very fast and accurate.

This paper addresses the problem of automated skin lesion segmentation in dermoscopy images. We propose a novel Multi-Scale Fully Convolutional DenseNets (MSFCDN) for skin lesion segmentation. The MSFCDN adopts fully convolutional architecture, which after training, can perform semantic segmentation of an image with arbitrary size. We conduct extensive experiments on ISBI 2017 “Skin Lesion Analysis Towards Melanoma Detection” Challenge dataset. Our method achieves an average Dice coefficient of 86.9% and an average accuracy of 95.3% for skin lesion segmentation.

304 A residual dense network for skin lesion segmentation in dermoscopy images

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%.

PMJAF-Net: Pyramidal multi-scale joint attention and adaptive fusion network for explainable skin lesion segmentation

Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effectively; however, the automated localization and segmentation of skin lesion at earlier stages is still a challenging task due to the low contrast between melanoma moles and skin portion and a higher level of color similarity between melanoma-affected and -nonaffected areas. In this paper, we present a fully automated method for segmenting the skin melanoma at its earliest stage by employing a deep-learning-based approach, namely faster region-based convolutional neural networks (RCNN) along with fuzzy k-means clustering (FKM). Several clinical images are utilized to test the presented method so that it may help the dermatologist in diagnosing this life-threatening disease at its earliest stage. The presented method first preprocesses the dataset images to remove the noise and illumination problems and enhance the visual information before applying the faster-RCNN to obtain the feature vector of fixed length. After that, FKM has been employed to segment the melanoma-affected portion of skin with variable size and boundaries. The performance of the presented method is evaluated on the three standard datasets, namely ISBI-2016, ISIC-2017, and PH2, and the results show that the presented method outperforms the state-of-the-art approaches. The presented method attains an average accuracy of 95.40, 93.1, and 95.6% on the ISIC-2016, ISIC-2017, and PH2 datasets, respectively, which is showing its robustness to skin lesion recognition and segmentation.