Segmentation of skin lesions image based on U-Net + +

Zhao, Chen; Shuai, Renjun; Ma, Li; Liu, Wenjia; Wu, Menglin

doi:10.1007/s11042-022-12067-z

Cited by 16 publications

(11 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the start, a keypoint extractor unit is used which extracts the reliable feature vector that is employed to estimate the heatmaps (Hms), embeddings, offset, and class (C). The Hms is concerned to give the approximation if a specific location in a U-Net + SVM Categorization ISIC-2016 76% Daghrir et al [23] SIFT + SVM and KNN Categorization ISIC-2017 88.40% Bama et al [24] GMM model Segmentation PH2 86.83% Hu et al [25] SIFT + SVM Categorization PH2 82% Durgarao et al [44] LVP, and LBP + C-means Segmentation PH2 79.44% DL techniques Ameri et al [26] AlexNet Categorization HAM10000 84% Acosta et al [27] ResNet-152 Categorization ISIC-2017 90.40% Zhang et al [28] VGG-16 Categorization ISIC-2017 92.72% Shan et al [29] FC-DPN Segmentation ISIC-2017 95.14% Bi et al [30] Res-FCN Segmentation ISIC-2016 95.78% Adegun et al [31] Encoder-decoder Categorization ISIC-2017 95% Nawaz et al [32] Faster-RCNN + FKM Segmentation PH2 95.6% Nawaz et al [35] Faster-RCNN + SVM Categorization ISIC-2016 89.10% Banerjee et al [36] YOLO + L-type fuzzy clustering Segmentation ISIC-2017 97.33% Iqbal et al [37] CNN Categorization ISIC-2019 88.75% Khan et al [38] Mask-RCNN, DenseNet201 + SVM Segmentation ISIC-2016 93.6% Mohakud et al [39] Encoder-decoder Segmentation ISIC-2016 98.32% Abdar et al [40] Bayesian model Categorization Kaggle skin cancer dataset 88.95% Pacheco et al [41] Metadata and block-based method Categorization ISIC-2019 74.90% Wang et al [42] U-Net Segmentation ISIC-2017 94.67% Zhao et al [43] U-Net++ Segmentation ISIC-2018 95.30% Ali et al [46] DCNN Categorization HAM10000 91.93% 5 Computational and Mathematical Methods in Medicine sample is a TL/BR corner associated with a particular category [51], while the embeddings are used to discriminate the detected pairs of corners and offsets to fine-tune the box position. The corners with high-scored TL and BR coordinates are employed to regulate the exact position of the box, whereas the associated category for each detected diseased region is specified by using the embedding distances on the computed feature vector.…”

Section: Methodsmentioning

confidence: 99%

“…The method elaborated in [38] attained the clustering and categorization results of 93.60% and 96.30%, correspondingly, however, at the expense of increased model complexity. Many other researchers have attempted to classify and segment the skin cancer moles [39][40][41][42][43][44][45]; however, there is a demand for performance enhancement. Besides, the expense of processing power for such methods is a substantial barrier in medical applications.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy $K$ -Means Clustering

Nawaz

Nazir

Khan

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Melanoma is a dangerous form of skin cancer that results in the demise of patients at the developed stage. Researchers have attempted to develop automated systems for the timely recognition of this deadly disease. However, reliable and precise identification of melanoma moles is a tedious and complex activity as there exist huge differences in the mass, structure, and color of the skin lesions. Additionally, the incidence of noise, blurring, and chrominance changes in the suspected images further enhance the complexity of the detection procedure. In the proposed work, we try to overcome the limitations of the existing work by presenting a deep learning (DL) model. Descriptively, after accomplishing the preprocessing task, we have utilized an object detection approach named CornerNet model to detect melanoma lesions. Then the localized moles are passed as input to the fuzzy K -means (FLM) clustering approach to perform the segmentation task. To assess the segmentation power of the proposed approach, two standard databases named ISIC-2017 and ISIC-2018 are employed. Extensive experimentation has been conducted to demonstrate the robustness of the proposed approach through both numeric and pictorial results. The proposed approach is capable of detecting and segmenting the moles of arbitrary shapes and orientations. Furthermore, the presented work can tackle the presence of noise, blurring, and brightness variations as well. We have attained the segmentation accuracy values of 99.32% and 99.63% over the ISIC-2017 and ISIC-2018 databases correspondingly which clearly depicts the effectiveness of our model for the melanoma mole segmentation.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy $K$ -Means Clustering

Nawaz

Nazir

Khan

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…This work 36 reported the highest segmentation and classification accuracy of 93.6% and 96.3%, respectively, on the ISBI2016 database; however, it is computationally costly. Some other studies also performed skin lesion classification and segmentation 37–43 . We have presented the critical analysis of existing approaches for the noma moles recognition in Table 1.…”

Section: Related Workmentioning

confidence: 99%

“…Some other studies also performed skin lesion classification and segmentation. [37][38][39][40][41][42][43] We have presented the critical analysis of existing approaches for the noma moles recognition in Table 1.…”

Section: Related Workmentioning

confidence: 99%

Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network

Nawaz

Nazir

Masood

et al. 2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Melanoma is the most fatal type of skin cancer which can cause the death of victims at the advanced stage. Extensive work has been presented by the researcher on computer vision for skin lesion localization. However, correct and effective melanoma segmentation is still a tough job because of the extensive variations found in the shape, color, and sizes of skin moles. Moreover, the presence of light and brightness variations further complicates the segmentation task. We have presented improved deep learning (DL)-based approach, namely, the DenseNet77-based UNET model. More clearly, we have introduced the DenseNet77 network at the encoder unit of the UNET approach to computing the more representative set of image features. The calculated keypoints are later segmented by the decoder of the UNET model. We have used two standard datasets, namely, the ISIC-2017 and ISIC-2018 to evaluate the performance of the proposed approach and acquired the segmentation accuracies of 99.21% and 99.51% for the ISIC-2017 and ISIC-2018 datasets, respectively. We have confirmed through both the quantitative and qualitative results that the proposed improved UNET approach is robust to skin lesions segmentation and can accurately recognize the moles of varying colors and sizes.

show abstract

“…Convolutional neural network (CNN) have made numerous achievements in the field of medical image segmentation, especially since the emergence of UNet [13,14], which has brought a new era of development in medical image segmentation. UNet framework and its several variants have drawn a lot of attention since such methods can collect local and global context data and owing to robustness, efficiency, interpretability, reliable computational cost, among other deep learning methods [14][15][16][17][18][19][20][21]. In comparison to traditional methods, the use of CNNs to support image segmentation in clinical perspectives has gained a lot of attention.…”

Section: Introductionmentioning

confidence: 99%

MSPAN: Multi‐scale pyramid attention network for efficient skin cancer lesion segmentation

Ahmed,

Xin,

Lizhuang

2024

IET Image Processing

View full text Add to dashboard Cite

Skin cancer is common and deadly, needs to be detected and treated properly. Deep learning algorithms like UNet have shown potential results in medical imaging. Such approaches still struggle to capture fine‐grained details and scale differences in skin lesions‐based occlusions' appearance, size etc. This research proposes a redesign UNet, the Multi‐Scale Pyramid Attention Network (MSPAN), to improve skin cancer lesion segmentation. The input data is processed at numerous scales with varied receptive fields. This enhances the network's ability to identify lesion locations by capturing local and global context. Attention approaches also help the network to suppress noise by focusing on informative features. We have evaluated MSPAN model on the publicly available ISIC2018 benchmark dataset for skin lesion segmentation. The method surpasses traditional UNet and other current methods in accuracy and effectiveness. The model also has a post‐processing to estimate lesion area for fast inference, making it suitable for extensive screening. Redesigned UNet with the Multi‐Scale Pyramid Attention Network improves skin cancer lesion segmentation. The model's ability to collect fine‐grained information and handle occlusions allows for more accurate skin cancer diagnosis and treatment. The MSPAN design can improve computer‐aided diagnosis systems and help dermatologists make precise clinical decisions.

show abstract

Segmentation of skin lesions image based on U-Net + +

Cited by 16 publications

References 54 publications

MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy $K$ -Means Clustering

MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy $K$ -Means Clustering

Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network

MSPAN: Multi‐scale pyramid attention network for efficient skin cancer lesion segmentation

Contact Info

Product

Resources

About

Segmentation of skin lesions image based on U-Net + +

Cited by 16 publications

References 54 publications

MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy K -Means Clustering

MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy K -Means Clustering

Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network

MSPAN: Multi‐scale pyramid attention network for efficient skin cancer lesion segmentation

Contact Info

Product

Resources

About

MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy $K$ -Means Clustering

MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy $K$ -Means Clustering