5,895 research outputs found
Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition
Deep learning techniques have shown their superior performance in
dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a
challenging task due to the difficulty of incorporating the useful
dermatologist clinical knowledge into the learning process. In this paper, we
propose a novel knowledge-aware deep framework that incorporates some clinical
knowledge into collaborative learning of two important melanoma diagnosis
tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically,
to exploit the knowledge of morphological expressions of the lesion region and
also the periphery region for melanoma identification, a lesion-based pooling
and shape extraction (LPSE) scheme is designed, which transfers the structure
information obtained from skin lesion segmentation into melanoma recognition.
Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma
recognition to skin lesion segmentation, an effective diagnosis guided feature
fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual
learning mechanism that further promotes the inter-task cooperation, and thus
iteratively improves the joint learning capability of the model for both skin
lesion segmentation and melanoma recognition. Experimental results on two
publicly available skin lesion datasets show the effectiveness of the proposed
method for melanoma analysis.Comment: Pattern Recognitio
Melanoma segmentation using deep learning with test-time augmentations and conditional random fields
In a computer-aided diagnostic (CAD) system for skin lesion segmentation, variations in shape and size of the skin lesion makes the segmentation task more challenging. Lesion segmentation is an initial step in CAD schemes as it leads to low error rates in quantification of the structure, boundary, and scale of the skin lesion. Subjective clinical assessment of the skin lesion segmentation results provided by current state-of-the-art deep learning segmentation techniques does not offer the required results as per the inter-observer agreement of expert dermatologists. This study proposes a novel deep learning-based, fully automated approach to skin lesion segmentation, including sophisticated pre and postprocessing approaches. We use three deep learning models, including UNet, deep residual U-Net (ResUNet), and improved ResUNet (ResUNet++). The preprocessing phase combines morphological filters with an inpainting algorithm to eliminate unnecessary hair structures from the dermoscopic images. Finally, we used test time augmentation (TTA) and conditional random field (CRF) in the postprocessing stage to improve segmentation accuracy. The proposed method was trained and evaluated on ISIC-2016 and ISIC-2017 skin lesion datasets. It achieved an average Jaccard Index of 85.96% and 80.05% for ISIC-2016 and ISIC-2017 datasets, when trained individually. When trained on combined dataset (ISIC-2016 and ISIC-2017), the proposed method achieved an average Jaccard Index of 80.73% and 90.02% on ISIC-2017 and ISIC-2016 testing datasets. The proposed methodological framework can be used to design a fully automated computer-aided skin lesion diagnostic system due to its high scalability and robustness
SkinNet: A Deep Learning Framework for Skin Lesion Segmentation
There has been a steady increase in the incidence of skin cancer worldwide,
with a high rate of mortality. Early detection and segmentation of skin lesions
are crucial for timely diagnosis and treatment, necessary to improve the
survival rate of patients. However, skin lesion segmentation is a challenging
task due to the low contrast of lesions and their high similarity in terms of
appearance, to healthy tissue. This underlines the need for an accurate and
automatic approach for skin lesion segmentation. To tackle this issue, we
propose a convolutional neural network (CNN) called SkinNet. The proposed CNN
is a modified version of U-Net. We compared the performance of our approach
with other state-of-the-art techniques, using the ISBI 2017 challenge dataset.
Our approach outperformed the others in terms of the Dice coefficient, Jaccard
index and sensitivity, evaluated on the held-out challenge test data set,
across 5-fold cross validation experiments. SkinNet achieved an average value
of 85.10, 76.67 and 93.0%, for the DC, JI, and SE, respectively.Comment: 2 pages, submitted to NSS/MIC 201
Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things
Introduction
Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques.
Method
This research uses a hybrid deep learning model that combines two cutting-edge approaches: Mask Region-based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset.
Results
The experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state-of-the-art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well.
Conclusion
In conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.© 2023 The Authors. Skin Research and Technology published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed
Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network
Skin lesion is a severe disease in world-wide extent. Early detection of
melanoma in dermoscopy images significantly increases the survival rate.
However, the accurate recognition of melanoma is extremely challenging due to
the following reasons, e.g. low contrast between lesions and skin, visual
similarity between melanoma and non-melanoma lesions, etc. Hence, reliable
automatic detection of skin tumors is very useful to increase the accuracy and
efficiency of pathologists. International Skin Imaging Collaboration (ISIC) is
a challenge focusing on the automatic analysis of skin lesion. In this paper,
we proposed two deep learning methods to address all the three tasks announced
in ISIC 2017, i.e. lesion segmentation (task 1), lesion dermoscopic feature
extraction (task 2) and lesion classification (task 3). A deep learning
framework consisting of two fully-convolutional residual networks (FCRN) is
proposed to simultaneously produce the segmentation result and the coarse
classification result. A lesion index calculation unit (LICU) is developed to
refine the coarse classification results by calculating the distance heat-map.
A straight-forward CNN is proposed for the dermoscopic feature extraction task.
To our best knowledges, we are not aware of any previous work proposed for this
task. The proposed deep learning frameworks were evaluated on the ISIC 2017
testing set. Experimental results show the promising accuracies of our
frameworks, i.e. 0.718 for task 1, 0.833 for task 2 and 0.823 for task 3 were
achieved.Comment: ISIC201
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