Deep learning for skin lesion segmentation

Mishra, Rashika; Daescu, Ovidiu

doi:10.1109/bibm.2017.8217826

Cited by 37 publications

(17 citation statements)

References 16 publications

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“…Thanks to the discovery and development of new methods for the precise identification of skin cancer, extensive research has been carried out in various areas. Many tactics are employed to the skin laceration segmentation tricky, including active contour approaches [3], the threshold method [4], and other machine learning generated methods [5][6][7][8][9]. A common non-learning method for skin segmentation is the Otsu method [10,11].…”

Section: Related Workmentioning

confidence: 99%

Skin Lesions Detection and Classification Using Deep Learning

2021

IJATCSE

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The skin lesion is considered the most widespread malignant disease in individuals, and melanoma is the deadliest form of the disease. Early detection affects the prognosis of the disease and improves the chances of survival. Dermatologists use scientific calculation tools such as ABCD to diagnose melanoma through visual inspection of the mole. However, computer vision tools have been presented to support the quantitative scrutiny of skin lesions. Significant improvements to deep learning algorithms in image recognition tasks should be very successful in medical image examination, particularly in the classification of skin lesions used to diagnose melanoma. In this research, a deep learning simulation with 38 layers to detect and classify skin lesions was proposed. Two datasets were used for training and testing i.e., the HAM10000 dataset & the ISIC2019 dataset. Experimental results show that the model outperforms on both the datasets hence making it nondependent of the dataset. 94.45% of validation top 3 accuracies are achieved on the HAM10000 dataset & 93.06% of validation top 3 accuracies are achieved on the ISIC2019 dataset.

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Section: Related Workmentioning

confidence: 99%

Skin Lesions Detection and Classification Using Deep Learning

2021

IJATCSE

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“…Berseth et al [38] developed a U-Net architecture for segmenting skin lesions based on the probability map of the image dimension where the ten-fold cross validation technique was used for training the model. Mishra [17] presented a deep learning technique for extracting the lesion region from dermoscopic images.…”

Section: Segmentation Techniquesmentioning

confidence: 99%

“…From the above details pertaining to the incidence and mortality rate associated with melanoma, timely diagnosis becomes all the more necessary for providing effective treatment to the affected. Insofar as the detection and segmentation of lesion boundaries, there are two streams of methodologies: first, traditional methods that usually resort to visual inspection by the clinician, and second, semi-automated and automated methods, which mostly involve point-based pixel intensity operations [9,10], pixel clustering methods [11][12][13][14], level set methods [15], deformable models [16], deep-learning based methods [17][18][19], et cetera.…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network

Zafar

Gilani

Waris

et al. 2020

Sensors

101

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Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggressive in nature due to its high metastasis rate. Therefore, timely diagnosis is critical for its treatment before the onset of malignancy. To address this problem, medical imaging is used for the analysis and segmentation of lesion boundaries from dermoscopic images. Various methods have been used, ranging from visual inspection to the textural analysis of the images. However, accuracy of these methods is low for proper clinical treatment because of the sensitivity involved in surgical procedures or drug application. This presents an opportunity to develop an automated model with good accuracy so that it may be used in a clinical setting. This paper proposes an automated method for segmenting lesion boundaries that combines two architectures, the U-Net and the ResNet, collectively called Res-Unet. Moreover, we also used image inpainting for hair removal, which improved the segmentation results significantly. We trained our model on the ISIC 2017 dataset and validated it on the ISIC 2017 test set as well as the PH2 dataset. Our proposed model attained a Jaccard Index of 0.772 on the ISIC 2017 test set and 0.854 on the PH2 dataset, which are comparable results to the current available state-of-the-art techniques.

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“…Although visual inspection proved positive in the non-invasive early diagnosis of melanoma, the limits of the human visual capacity lead to inaccuracies due to the morphological structures and their side effects. The inaccuracies might be due to many factors; among them, some are: hair presence, variable lesion sizes, colour and shape, irregular boundaries of asymmetrical lesions, or poor contrast regarding the normal skin and the lesion [5]. The interest in automatic image analysis methods is twofold: the relevance of quantitative information for a lesion and playing as an early warning tool.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network and Fuzzy Logic-based Hybrid Melanoma Diagnosis System

Yalcinkaya

Erbas

2021

ELEKTRON ELEKTROTECH

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Studies on the detection of early stage melanoma have recently gained significant interest. Computer aided diagnosis systems based on neural networks, machine learning, convolutional neural networks (CNNs), and deep learning help early stage detection considerably. The colour and shapes of the images created by the pixels are crucial for the CNNs, as the pixels and associated pictures are interrelated just as a person’s fingerprint is unique. By observing this relationship, the pixel values of each picture with its neighborhoods were determined by a fuzzy logic-based system and a unique fingerprint matrix named Fuzzy Correlation Map (FCov-Map) was produced. The fuzzy logic system has four inputs and one output. The advantage of CNNs trained with fuzzy covariance maps is to eliminate both the limited availability of medical grade training data and the need for extensive image preprocessing. The fuzzy logic output is fed to the pretrained AlexNet CNN algorithm. To deliver a reliable result, a deep CNN needs a large amount of data to process. However, to obtain and use the required sufficient data for diseases is not cost- and time-effective. Therefore, the suggested fuzzy logic-based fuzzy correlation map is tackling this issue to solve the limitedness of training CNN data set.

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Deep learning for skin lesion segmentation

Cited by 37 publications

References 16 publications

Skin Lesions Detection and Classification Using Deep Learning

Skin Lesions Detection and Classification Using Deep Learning

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network

Convolutional Neural Network and Fuzzy Logic-based Hybrid Melanoma Diagnosis System

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