Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks

Al‐masni, Mohammed A.; Al-antari, Mugahed A.; Choi, Mun-Taek; Han, Sangheon; Kim, Tae‐Seong

doi:10.1016/j.cmpb.2018.05.027

Cited by 391 publications

(209 citation statements)

References 47 publications

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“…Also, it can be observed that the closer best-segmented result was that of the Al-masni et al [21] in terms of ACC, DIC, and JAC. For SPE and SEN, the results of Barata et al [32] and Rastgoo et al [33] were the closest.…”

Section: Resultsmentioning

confidence: 51%

“…Yuan et al [14] presented a skin lesion segmentation technique by using deep Fully Convolutional Network (FCN) that can segment three samples of skin lesions (for example, healthy moles and unhealthy moles). Al-masni et al [21] used various dermoscopy datasets for a skin lesion full resolution convolutional network (FrCN) for a better performance when compared with existing methods of melanoma detection that leads to an enhancement in the segmentation analysis. The results in Reference [6] established that social group optimization (SGO) is very active in extracting lesion sectors from hairy skin tumor images compared to the alternatives studied for detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm

et al. 2018

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Abstract:The occurrence rates of melanoma are rising rapidly, which are resulting in higher death rates. However, if the melanoma is diagnosed in Phase I, the survival rates increase. The segmentation of the melanoma is one of the largest tasks to undertake and achieve when considering both beneath and over the segmentation. In this work, a new approach based on the artificial bee colony (ABC) algorithm is proposed for the detection of melanoma from digital images. This method is simple, fast, flexible, and requires fewer parameters compared with other algorithms. The proposed approach is applied on the PH2, ISBI 2016 challenge, the ISBI 2017 challenge, and Dermis datasets. These bases contained images are affected by different abnormalities. The formation of the databases consists of images collected from different sources; they are bases with different types of resolution, lighting, etc., so in the first step, the noise was removed from the images by using morphological filtering. In the next step, the ABC algorithm is used to find the optimum threshold value for the melanoma detection. The proposed approach achieved good results in the conditions of high specificity. The experimental results suggest that the proposed method accomplished higher performance compared to the ground truth images supported by a Dermatologist. For the melanoma detection, the method achieved an average accuracy and Jaccard's coefficient in the range of 95. 24-97.61%, and 83.56-85.25% in these four databases. To show the robustness of this work, the results were compared to existing methods in the literature for melanoma detection. High values for estimation performance confirmed that the proposed melanoma detection is better than other algorithms, which demonstrates the highly differential power of the newly introduced features.

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Section: Resultsmentioning

confidence: 51%

Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm

et al. 2018

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“…Visual assessment of the segmentation accuracy of Faster-RCNN+SkinNet relative to SkinNet, depicted in Fig.3, confirms the superiority of the former relative to the latter. Furthermore, for the PH2 dataset, our method once again outperformed a state-of-the-art approach [16], in terms of AC, DC, JI and SE, highlighting its ability to generalize to images acquired from other databases. These results and comparisons, clearly outline the improvement in segmentation accuracy achieved by the proposed approach, relative to the state-of-the-art, and by extension, the benefit of formulating a multi-task learning approach, for skin lesion segmentation.…”

Section: Resultsmentioning

confidence: 76%

A Multi-task Framework for Skin Lesion Detection and Segmentation

Vesal

Patil

Ravikumar

et al. 2018

Lecture Notes in Computer Science

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Early detection and segmentation of skin lesions is crucial for timely diagnosis and treatment, necessary to improve the survival rate of patients. However, manual delineation is time consuming and subject to intra-and inter-observer variations among dermatologists. This underlines the need for an accurate and automatic approach to skin lesion segmentation. To tackle this issue, we propose a multi-task convolutional neural network (CNN) based, joint detection and segmentation framework, designed to initially localize the lesion and subsequently, segment it. A 'Faster region-based convolutional neural network' (Faster-RCNN) which comprises a region proposal network (RPN), is used to generate bounding boxes/region proposals, for lesion localization in each image. The proposed regions are subsequently refined using a softmax classifier and a bounding-box regressor. The refined bounding boxes are finally cropped and segmented using 'SkinNet', a modified version of U-Net. We trained and evaluated the performance of our network, using the ISBI 2017 challenge and the PH2 datasets, and compared it with the state-of-the-art, using the official test data released as part of the challenge for the former. Our approach outperformed others in terms of Dice coefficients (> 0.93), Jaccard index (> 0.88), accuracy (> 0.96) and sensitivity (> 0.95), across five-fold cross validation experiments.Recent trends indicate a growing number of skin cancer diagnoses worldwide, each year. In 2016, approximately 80,000 new cases of skin cancer were expected to be diagnosed, with 10,000 melanoma related deaths (the most aggressive form of skin cancer), in the USA alone [1]. Clinical screening and diagnosis typically involve examination by an expert dermatologist, followed by histopathological analysis of biopsies. These steps however, invariably suffer from high inter-rater and inter-center variability, and studies have shown that patient survival rates improve to over 95%, following early detection and diagnosis of melanomas. To

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“…Consequently, this may explain the lower probability (AUC) values obtained. In SegNet for skin lesion segmentation implemented by Al-masni et al (2018), the authors store the indices at each max-pooling layer in the encoder which are later used to upsample the corresponding feature map in the decoder in order to preserve the high-frequency information. Nevertheless, the authors do not take the neighbouring information into account during upsampling.…”

Section: Advantages Of Dsnet Over Recent Networkmentioning

confidence: 99%

DSNet: Automatic dermoscopic skin lesion segmentation

Hasan

Dahal

Samarakoon

et al. 2020

Computers in Biology and Medicine

140

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BackgroundAutomatic segmentation of skin lesion is considered a crucial step in Computer Aided Diagnosis (CAD) for melanoma diagnosis. Despite its significance, skin lesion segmentation remains a challenging task due to their diverse color, texture, and indistinguishable boundaries and forms an open problem. MethodsThrough this study, we present a new and automatic semantic segmentation network for robust skin lesion segmentation named Dermoscopic Skin Network (DSNet). In order to reduce the number of parameters to make the network lightweight, we used depth-wise separable convolution in lieu of standard convolution to project the learnt discriminating features onto the pixel space at different stages of the encoder. Additionally, we implemented U-Net and Fully Convolutional Network (FCN8s) to compare against the proposed DSNet.

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Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks

Cited by 391 publications

References 47 publications

Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm

Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm

A Multi-task Framework for Skin Lesion Detection and Segmentation

DSNet: Automatic dermoscopic skin lesion segmentation

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