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1 |
Transform domain representation-driven convolutional neural networks for skin lesion segmentation
انتقال شبکه های عصبی کانولوشن نمایندگی محور دامنه برای تقسیم بندی ضایعه پوستی-2020 Automated diagnosis systems provide a huge improvement in early detection of skin cancer, and con- sequently, contribute to successful treatment. Recent research on convolutional neural network has achieved enormous success in segmentation and object detection tasks. However, these networks require large amount of data that is a big challenge in medical domain where often have insufficient data and even a pretrained model on medical images can be hardly found. Lesion segmentation as the initial step of skin cancer analysis remains a challenging issue since datasets are small and include a variety of im- ages in terms of light, color, scale, and marks which have led researchers to use extensive augmentation and preprocessing techniques or fine tuning the network with a pretrained model on irrelevant images. A segmentation model based on convolutional neural networks is proposed in this study for the tasks of skin lesion segmentation and dermoscopic feature segmentation. The network is trained from scratch and despite the small size of datasets neither excessive data augmentation nor any preprocessing to remove artifacts or enhance the images are applied. Alternatively, we investigated incorporating image represen- tations of the transform domain to the convolutional neural network and compared to a model with more convolutional layers that resulted in 6% higher Jaccard index and has shorter training time. The model improved by applying CIELAB color space and the performance of the final proposed architecture is evaluated on publicly available datasets from ISBI challenges in 2016 and 2017. The proposed model has resulted in an improvement of as much as 7% for the segmentation metrics and 17% for the fea- ture segmentation, which demonstrates the robustness of this unique hybrid framework and its future applications as well as further improvement. Keywords: Convolutional neural network | Dermoscopic features | Melanoma | Skin lesion segmentation | Transform domain |
مقاله انگلیسی |
2 |
Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images
Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images-2019 Abstract Background: The diagnosis of most cancers is made by a board-certified pathologist
based on a tissue biopsy under the microscope. Recent research reveals a high discordance
between individual pathologists. For melanoma, the literature reports on 25e26% of discordance
for classifying a benign nevus versus malignant melanoma. A recent study indicated
the potential of deep learning to lower these discordances. However, the performance of deep
learning in classifying histopathologic melanoma images was never compared directly to human
experts. The aim of this study is to perform such a first direct comparison.
Methods: A total of 695 lesions were classified by an expert histopathologist in accordance
with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides of these lesions were digitalised via a slide scanner and then randomly cropped. A total of 595
of the resulting images were used to train a convolutional neural network (CNN). The additional
100 H&E image sections were used to test the results of the CNN in comparison to 11
histopathologists. Three combined McNemar tests comparing the results of the CNNs test
runs in terms of sensitivity, specificity and accuracy were predefined to test for significance
(p < 0.05).
Findings: The CNN achieved a mean sensitivity/specificity/accuracy of 76%/60%/68% over 11
test runs. In comparison, the 11 pathologists achieved a mean sensitivity/specificity/accuracy
of 51.8%/66.5%/59.2%. Thus, the CNN was significantly (p Z 0.016) superior in classifying
the cropped images.
Interpretation: With limited image information available, a CNN was able to outperform 11
histopathologists in the classification of histopathological melanoma images and thus shows
promise to assist human melanoma diagnoses. KEYWORDS : Melanoma | Pathology | Histopathology | Deep learning | Artificial intelligence |
مقاله انگلیسی |
3 |
Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope
تشخیص سرطان پوست با الگوریتم های یادگیری عمیق و آنالیز صدا: یک مطالعه بالینی آینده نگر از یک درموسکوپ ابتدایی-2019 Background: Skin cancer (SC), especiallymelanoma, is a growing public health burden. Experimental studies have
indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities.
Previously, it was demonstrated that diagnostics by dermoscopy are improved by applying an additional
sonification (data to sound waves conversion) layer on DL algorithms. The aim of the study was to determine
the impact of image quality on accuracy of diagnosis by sonification employing a rudimentary skin magnifier
with polarized light (SMP).
Methods: Dermoscopy images acquired by SMP were processed by a first deep learning algorithm and sonified.
Audio output was further analyzed by a different secondary DL. Study criteria outcomes of SMP were specificity
and sensitivity,which were further processed by a F2-score, i.e. applying a twice extra weight to sensitivity over
positive predictive values.
Findings: Patients (n=73) fulfilling inclusion criteriawere referred to biopsy. SMP analysis metrics resulted in a
receiver operator characteristic curve AUCs of 0.814 (95% CI, 0.798–0.831). SMP achieved a F2-score sensitivity of
91.7%, specificity of 41.8% and positive predictive value of 57.3%.Diagnosing the sameset of patients lesions by an
advanced dermoscope resulted in a F2-score sensitivity of 89.5%, specificity of 57.8% and a positive predictive
value of 59.9% (P=NS).
Interpretation: DL processing of dermoscopic images followed by sonification results in an accurate diagnostic
output for SMP, implying that the quality of the dermoscope is not the major factor influencing DL diagnosis of
skin cancer. Present system might assist all healthcare providers as a feasible computer-assisted detection system.
Fund: Bostel Technologies. Keywords: Skin cancer | Deep learning | Dermoscopy | Sonification | Melanoma | Telemedicine | Artificial intelligence |
مقاله انگلیسی |
4 |
A comparative study of deep learning architectures on melanoma detection
مطالعه تطبیقی معماریهای یادگیری عمیق در تشخیص ملانوما-2019 Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early
detection of melanoma can reduce the morbidity and mortality associated with skin cancer. Dermoscopic images
acquired by dermoscopic instruments are used in computational analysis for skin cancer detection. However,
some image quality limitations such as noises, shadows, artefacts exist that could compromise the robustness of
the skin image analysis. Hence, developing an automatic intelligent system for skin cancer diagnosis with accurate
detection rate is crucial. In this paper, we evaluate the performance of several state-of-the-art convolutional
neural networks in dermoscopic images of skin lesions. Our experiment is conducted on a graphics processing
unit (GPU) to speed up the training and deployment process. To enhance the quality of images, we
employ different pre-processing steps. We also apply data augmentation methodology such as horizontal and
vertical flipping techniques to address the class skewness problem. Both pre-processing and data augmentation
could help to improve the final accuracy. Keywords: Cancer classification | Computational diagnosis | Convolutional neural networks | Deep learning | Melanoma detection |
مقاله انگلیسی |
5 |
Multi-Class Multi-Level Classification Algorithm for Skin Lesions Classification using Machine Learning Techniques
الگوریتم طبقه بندی چند مرحله ای چند سطح برای طبقه بندی ضایعات پوستی با استفاده از تکنیک های یادگیری ماشین-2019 Skin diseases remain a major cause of disability worldwide and contribute approximately 1.79% of the global
burden of disease measured in disability-adjusted life years. In the United Kingdom alone, 60% of the
population suffer from skin diseases during their lifetime. In this paper, we propose an intelligent digital
diagnosis scheme to improve the classification accuracy of multiple diseases. A Multi-Class Multi-Level
(MCML) classification algorithm inspired by the “divide and conquer” rule is explored to address the research
challenges. The MCML classification algorithm is implemented using traditional machine learning and
advanced deep learning approaches. Improved techniques are proposed for noise removal in the traditional
machine learning approach. The proposed algorithm is evaluated on 3672 classified images, collected from
different sources and the diagnostic accuracy of 96.47% is achieved. To verify the performance of the proposed
algorithm, its metrics are compared with the Multi-Class Single-Level classification algorithm which is the
main algorithm used in most of the existing literature. The results also indicate that the MCML classification
algorithm is capable of enhancing the classification performance of multiple skin lesions. Keywords: skin lesion classification | computer-aided diagnosis | machine learning | deep learning | texture & colour features | melanoma classification | eczema classification |
مقاله انگلیسی |