Hybrid fully convolutional networks‐based skin lesion segmentation and melanoma detection using deep feature

Jayapriya, K.; Jacob, I. Jeena

doi:10.1002/ima.22377

Cited by 58 publications

(17 citation statements)

References 62 publications

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“…The area of interest which contains the skin lesion has been resized, cropped and transferred for melanoma classification. Moreover, Jayapriya et al [47] employ a hybrid framework that includes two FCNs (VGG 16 & GoogleNet). The classification was performed using deep residual network and a hand-crafted tool to remove the feature from the segmented lesion.…”

Section: C) Fully Convolutional Network (Fcn) Based Methodsmentioning

confidence: 99%

“…This method has used non-dermoscopic images for diagnosis. Jayapriya et al [47], uses transfer learning which was based on GoogleNet and VGG16. To validate the proposed method ISBI 2016 and 2017 datasets were used.…”

Section: B) Efficiency Calculation On Multiple Datasetsmentioning

confidence: 99%

“…The research articles [47], [48], [54], [55], [59], [61], [64], [68], [73], [75] and [78] In this review [67], [70], [72], [76] and [94] use this dataset to check the accuracy of their methods e) ISBI 2017 Dataset Challenge [37]: This dataset was used by [47], [50], [58], [59], [63], [64], [71] and [77] for testing their proposed methods. It contained 2,000 dermoscopic images in which 374 were melanomas, 254 were seborrheic keratoses, and 1,372 benign nevi images.…”

Section: A) Isbi Challenge 2016 Dataset [5]mentioning

confidence: 99%

See 2 more Smart Citations

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

et al. 2020

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Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. This study provides a systematic literature review of the latest research on melanoma classification using CNN. We restrict our study to the binary classification of melanoma. In particular, this research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets. We conducted a systematic review of existing literature, identifying the literature through a systematic search of the IEEE, Medline, ACM, Springer, Elsevier, and Wiley databases. A total of 5112 studies were identified out of which 55 well-reputed studies were selected. The main objective of this study is to collect state of the art research which identify the recent research trends, challenges and opportunities for melanoma diagnosis and investigate the existing solutions for the diagnosis of melanoma detection using deep learning. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.

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Section: C) Fully Convolutional Network (Fcn) Based Methodsmentioning

confidence: 99%

Section: B) Efficiency Calculation On Multiple Datasetsmentioning

confidence: 99%

Section: A) Isbi Challenge 2016 Dataset [5]mentioning

confidence: 99%

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Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

et al. 2020

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“…Jayapariya et al [ 37 ] introduced a fully convolutional network-based model for melanoma detection. The VGG16 [ 38 ] and GoogleNet [ 39 ] deep CNN models were used for segmentation of lesions, followed by the feature extraction step.…”

Section: Related Workmentioning

confidence: 99%

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

et al. 2021

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Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

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“…Recently, DANet [35] integrates two attention modules on the basis of FCNs, and further improved the feature representation by adding the outputs of the two attention modules. Also, Jayapriya et al [36] combined two types of FCNs based on VGG-16 and GoogLeNet to formulate a hybrid framework. This two-stage hybrid framework completes the classification by extracting features with deep residual networks and handmade features.…”

Section: B Fully Convolutional Networkmentioning

confidence: 99%

A Hybrid Semantic Segmentation Based on Level-Set Evolution Driven by Fully Convolutional Networks

Wang

Fan

et al. 2021

IEEE Access

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Semantic segmentation network is one successful deep architecture for understanding key foregrounds. The fully convolutional networks (FCNs) can learn the regional masks by composing layered representations coarse-to-fine. Nevertheless, these increasing feature maps often fail to improve significantly the discrimination to segmentation details, due to the lack of accurate spatial representations and prior on salient foreground boundaries. This paper presents a hybrid semantic segmentation network based on a differentiable level-set evolving on multiple feature channels, which can be driven by the deep architectures of FCNs. To the level-set iterations, the re-initialization and Gaussian smoothing operations have been integrated to achieve an uniform training procedure. Also, this proposed wrapping level-set layer can be easily generalized to other existing deep segmentation architectures. Experimental results on various open datasets verify that, compared with related baselines, this hybrid framework significantly improves the spatial details and accuracy rates of the segmenting foreground boundaries.

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Hybrid fully convolutional networks‐based skin lesion segmentation and melanoma detection using deep feature

Cited by 58 publications

References 62 publications

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

A Hybrid Semantic Segmentation Based on Level-Set Evolution Driven by Fully Convolutional Networks

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