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

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.

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.

Automated skin lesion segmentation is essential to assist doctors in diagnosis. Most methods focus on lesion segmentation of dermoscopy images, while a few focus on clinical images. Nearly all the existing methods tackle the binary segmentation problem as to distinguish lesion parts from normal skin parts, and are designed for diseases with localized solitary skin lesion. Besides, the characteristics of both the dermoscopy images and the clinical images are four-fold: (1) Only one skin lesion exists in the image. (2) The skin lesion mostly appears in the center of the image. (3) The backgrounds are similar between different images of same modality. (4) The resolution of images isn’t high, with an average of about \(1500\times 1200\) in several popular datasets. In contrast, this paper focuses on a four-class segmentation task for Cutaneous T-cell lymphomas (CTCL), an extremely aggressive skin disease with three visually similar kinds of lesions. For the first time, we collect a new dataset, which only contains clinical images captured from different body areas of human. The main characteristics of these images differ from all the existing images in four aspects: (1) Multiple skin lesion parts exist in each image. (2) The skin lesion parts are widely scattered in different areas of the image. (3) The background of the images has a large variety. (4) All the images have high resolutions, with an average of \(3255 \times 2535\). According to the characteristics and difficulties of CTCL, we design a new Multi Knowledge Learning Network (MKLN). The experimental results demonstrate the superiority of our method, which meet the clinical needs.

The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided Diagnosis (CAD) systems in improving the diagnosing rate of skin cancer. The results of the existing skin lesion segmentation techniques are not up to the mark for dermoscopic images with artifacts like varying size corner borders with color similar to lesion(s) and/or hairs having low contrast with surrounding background. To improve the results of the existing skin lesion segmentation techniques for such kinds of dermoscopic images, an effective skin lesion segmentation method is proposed in this research work. The proposed method searches for the presence of corner borders in the given dermoscopc image and removes them if found otherwise it starts searching for the presence of hairs on it and eliminate them if present. Next, it enhances the resultant image using state-of-the-art image enhancement method and segments lesion from it using machine learning technique namely, GrabCut method. The proposed method was tested on PH2 and ISIC 2018 datasets containing 200 images each and its accuracy was measured with two evaluation metrics, i.e., Jaccard index, and Dice index. The evaluation results show that our proposed skin lesion segmentation method obtained Jaccard Index of 0.77, 0.80 and Dice index of 0.87, 0.82 values on PH2, and ISIC2018 datasets, respectively, which are better than state-of-the-art skin lesion segmentation techniques.

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.

Hyper-fusion network for semi-automatic segmentation of skin lesions.

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.

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.

Segmentation of skin lesion images using discrete wavelet transform

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.

Recently, the incidence of skin cancer has increased considerably and is seriously threatening human health. Automatic detection of this disease, where early detection is critical to human life, is quite challenging. Factors such as undesirable residues (hair, ruler markers), indistinct boundaries, variable contrast, shape differences, and color differences in the skin lesion images make automatic analysis quite difficult. To overcome these challenges, a highly effective segmentation method based on a fully convolutional network (FCN) is presented in this paper. The proposed improved FCN (iFCN) architecture is used for the segmentation of full-resolution skin lesion images without any pre- or post-processing. It is to support the residual structure of the FCN architecture with spatial information. This situation, which creates a more advanced residual system, enables more precise detection of details on the edges of the lesion, and an analysis independent of skin color can be performed. It offers two contributions: determining the center of the lesion and clarifying the edge details despite the undesirable effects. Two publicly available datasets, the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Challenge and PH2 datasets, are used to evaluate the performance of the iFCN method. The mean Jaccard index is 78.34%, the mean Dice score is 88.64%, and the mean accuracy value is 95.30% for the proposed method for the ISBI 2017 test dataset. Furthermore, the mean Jaccard index is 87.1%, the mean Dice score is 93.02%, and the mean accuracy value is 96.92% for the proposed method for the PH2 test dataset.

In recent years, intelligent automation in the healthcare sector becomes more familiar due to the integration of artificial intelligence (AI) techniques. Intelligent healthcare systems assist in making better decisions, which further enable the patient to provide improved medical services. At the same time, skin lesion is a deadly disease that affects people of all age groups. Skin lesion segmentation and classification play a vital part in the earlier and precise skin cancer diagnosis by intelligent systems. However, the automated diagnosis of skin lesions in dermoscopic images is challenging because of the problems such as artifacts (hair, gel bubble, ruler marker), blurry boundary, poor contrast, and variable sizes and shapes of the lesion images. This study develops intelligent multilevel thresholding with deep learning (IMLT-DL) based skin lesion segmentation and classification model using dermoscopic images to address these problems. Primarily, the presented IMLT-DL model incorporates the Top hat filtering and inpainting technique for the pre-processing of the dermoscopic images. In addition, the Mayfly Optimization (MFO) with multilevel Kapur’s thresholding-based segmentation process is involved in determining the infected regions. Besides, an Inception v3 based feature extractor is applied to derive a valuable set of feature vectors. Finally, the classification process is carried out using a gradient boosting tree (GBT) model. The presented model’s performance takes place against the International Skin Imaging Collaboration (ISIC) dataset, and the experimental outcomes are inspected in different evaluation measures. The resultant experimental values ensure that the proposed IMLT-DL model outperforms the existing methods by achieving higher accuracy of 0.992.

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

An automatic skin lesion segmentation algorithm not only facilitates the dermatologist’s workload on skin cancer analysis but also provides a platform for early cancer prediction. Over the years, several deep learning methods have been proposed to address the skin lesion segmentation problem. However, training deep models usually requires a large-scale annotated dataset, which is not feasible in the medical domain due to the annotation burden. In addition, the low data regime highly increases the overfitting potential for the neural network. To address these limitations in an end-to-end manner, we propose to incorporate unlabelled samples during the training process. Our network offers a semi-supervised training schema, wherein the first stage performs a supervised training strategy to learn semantic segmentation map while the second step focuses on the unsupervised technique to enrich the encoder module. Specifically, unlike the literature work on skin lesion segmentation, we design a surrogate task on top of the convolutional and Transformer representations to learn data-driven features from the image itself to alleviate the requirement of the large annotated dataset. The effectiveness of the proposed method is demonstrated using three different skin lesion segmentation datasets, namely ISIC 2018 (dice score 0.905), ISIC 2017 (dice score 0.898) and PH2 (dice score 0.940). Particularly we observed that including the unsupervised samples can increase the dice score by 2%.