PsLSNet: Automated psoriasis skin lesion segmentation using modified U-Net-based fully convolutional network

Dash, Manoranjan; Londhe, Narendra D.; Ghosh, Subhojit; Semwal, Ashish; Sonawane, Rajendra S.

doi:10.1016/j.bspc.2019.04.002

Cited by 78 publications

(34 citation statements)

References 20 publications

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“…It is almost exclusively used to examine skin diseases such as skin lesions. The primary medical condition diagnosed using Dermoscopy images in our survey is melanoma or skin cancer, though we have also found a single paper on psoriasis diagnosis [289]. The performance of Dermoscopy image analysis methods is of keen interest in the medical imaging community since it is often used for early detection of melanoma and is less costly than other noninvasive diagnostic tools.…”

Section: E Dermoscopymentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: E Dermoscopymentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…Their method also does not require any pre-or post-processing in order to segment lesion boundary. Dash et al [9] proposed an auto-mated method for psoriasis lesion segmentation by utilizing the modified architecture of U-Net, known as PsLSNet. PsLSNet is 29-layer deep fully CNN, which automatically extracts spatial information.…”

Section: Related Workmentioning

confidence: 99%

Towards accurate classification of skin cancer from dermatology images

Gautam

Raman

2021

IET Image Processing

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Skin cancer is the most well-known disease found in the individuals who are exposed to the Sun's ultraviolet (UV) radiations. It is identified when skin tissues on the epidermis grow in an uncontrolled manner and appears to be of different colour than the normal skin tissues. This paper focuses on predicting the class of dermascopic images as benign and malignant. A new feature extraction method has been proposed to carry out this work which can extract relevant features from image texture. Local and gradient information from x and y directions of images has been utilized for feature extraction. After that images are classified using machine learning algorithms by using those extracted features. The efficacy of the proposed feature extraction method has been proved by conducting several experiments on the publicly available image dataset 2016 International Skin Imaging Collaboration (ISIC 2016). The classification results obtained by the method are also compared with state-of-the-art feature extraction methods which show that it performs better than others. The evaluation criteria used to obtain the results are accuracy, true positive rate (TPR) and false positive rate (FPR) where TPR and FPR are used for generating receiver operating characteristic curves.

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“…Some previous studies have used FCNN models for skin lesion segmentation, 11,12,42 . Tschandl et al have designed a FCNN with U‐Net style architecture using pre‐trained ResNet34 as its encoding layers for lesion segmentation as a prerequisite step for skin cancer diagnosis from dermoscopy images 12 .…”

Section: Related Workmentioning

confidence: 99%

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

Khatibi

Rezaei

Fashtami

et al. 2020

Skin Research and Technology

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Background Vitiligo is an acquired pigmentary skin disorder characterized by depigmented macules and patches which brings many challenges for the patients suffering from. For vitiligo severity assessment, several scoring methods have been proposed based on morphometry and colorimetry. But, all methods suffer from much inter‐ and intra‐observer variations for estimating the depigmented area. For all mentioned assessment methods of vitiligo disorder, accurate segmentation of the skin images for lesion detection and localization is required. The image segmentation for localizing vitiligo skin lesions has many challenges because of illumination variation, different shapes and sizes of vitiligo lesions, vague lesion boundaries and skin hairs and vignette effects. The manual image segmentation is a tedious and time‐consuming task. Therefore, using automatic image segmentation methods for lesion detection is necessarily required. Materials and methods In this study, a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) methods is proposed for localizing vitiligo lesions in skin images. Unsupervised segmentation methods do not require prior manual segmentation of vitiligo lesions which is a tedious and time‐consuming task with intra‐ and inter‐observer variations. Results Our collected dataset includes 877 images taken from 21 patients with the resolution of 5760*3840 pixels suffering from vitiligo disorder. Experimental results show that SEDCIS outperforms the compared methods with accuracy of 97%, sensitivity of 98%, specificity of 96%, area overlapping of 94%, and Dice index of 97%. Conclusion The proposed method can segment vitiligo lesions with highly reasonable performance and can be used for assessing the vitiligo lesion surface.

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PsLSNet: Automated psoriasis skin lesion segmentation using modified U-Net-based fully convolutional network

Cited by 78 publications

References 20 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Towards accurate classification of skin cancer from dermatology images

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

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