Skin lesion segmentation using k-mean and optimized fire fly algorithm

Garg, Sachin; Jindal, Balkrishan

doi:10.1007/s11042-020-10064-8

Cited by 32 publications

(17 citation statements)

References 39 publications

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“…In most of the segmentation approaches, preprocessing was the major step that prepares the skin images for further processing. The unwanted artifacts such as illumination levels and surface hairs are removed to improve the segmentation quality [ 22 ]. Among various proposed methods, edge or border detection in digital images was the major challenge.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Thapar

Rakhra

Cazzato

et al. 2022

Journal of Healthcare Engineering

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Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals’ visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques’ results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Thapar

Rakhra

Cazzato

et al. 2022

Journal of Healthcare Engineering

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“…As methods [ 5 , 8 , 11 – 14 , 19 , 23 ] do not deal with images containing artifacts like hairs, and borders and the PH2 dataset contains images with borders and hairs, therefore their results’ accuracy in term of Jaccard index and dice index are not satisfactory.…”

Section: Resultsmentioning

confidence: 99%

“…In [ 19 ], the hairs and markers present on the dermoscopic image are removed using threshold and morphological processing at pre-processing level. The resultant pre-processed image is then segmented using K-mean clustering.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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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.

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“…A conventional method like K-means was applied by Alvarez et al [15], Agarwal et al [16] and Garg et al [17]. In Agarwal et al [16], the authors proposed an approach divided into three steps: (1) preprocessing; (2) segmentation, in which the K-means method is applied using two clusters, one to represent the lesion and another to represent the background skin (normal skin).…”

Section: Related Workmentioning

confidence: 99%

U-Net_based_Network_Applied_to_Skin_Lesion_Segmentation_An_Ablation_Study

Araújo

2022

CLEIej

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O câncer de pele é um dos cânceres que mais agrava os problemas de saúde pública. Dentre os tipos de câncer, o melanoma é o mais agressivo. Seu diagnóstico precoce é fundamental para aumentar a possibilidade de tratamento adequado, visando reduzir a taxa de mortalidade. Os dermatologistas geralmente usam métodos manuais para diagnosticar lesões de pele. Além de demorados por serem realizados manualmente, esses métodos podem apresentar resultados diferentes para a mesma lesão quando analisados ??por diferentes especialistas. Portanto, um diagnóstico automatizado pode ser necessário para lidar com esse problema e evitar testes invasivos. Segmentar a lesão cutânea na imagem dermatoscópica pode ser fundamental, pois se trata de umatarefa essencial no processo de análise de imagens. No presente trabalho, um modelo de Rede Neural Convolucional (CNN), baseado na U-Net, é proposto para segmentar a lesão em imagens dermatoscópicas . Este modelo obteve um Jaccard de 0,833 para a base ISIC 2017 e 0,850 para a base ISIC 2018. O modelo proposto possui uma arquitetura mais simples, exigindo menos recursos computacionais. Para verificar a eficácia do método proposto, realizamos um estudo de ablação e experimentos comparativos com outros métodos.

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Skin lesion segmentation using k-mean and optimized fire fly algorithm

Cited by 32 publications

References 39 publications

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

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

U-Net_based_Network_Applied_to_Skin_Lesion_Segmentation_An_Ablation_Study

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