Fair comparison of skin detection approaches on publicly available datasets

Lumini, Alessandra; Nanni, Loris

doi:10.1016/j.eswa.2020.113677

Cited by 33 publications

(54 citation statements)

References 52 publications

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“…For both the clinician and the patient, overlooking a lesion on a scan may have dire consequences. Thus, detection is a field of study requiring both accuracy and sensitivity [272][273][274]. Chouhan et al [275] introduced an innovative deep learning framework for the detection of pneumonia by adopting the idea of transfer learning.…”

Section: Detectionmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Section: Detectionmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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“…In our experiment, we carry out a comparison of several approaches performing a single training on a small dataset including only 2000 labeled images, while testing is performed on 11 different datasets including images from very different applications. The reported results show that the proposed ensembles reach state-of-the-art performance [17] in most of the benchmark datasets even without ad-hoc tuning.…”

Section: Introductionmentioning

confidence: 79%

“…The reported results show that all the proposed ensembles reach state of the art performance [17] in most of the benchmark datasets: all of them outperform our baseline ReLU.…”

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confidence: 79%

“…• ReLU is the standard DeepLabv3+ segmentation CNN based on ResNet50 encoder. This is our baseline, since it has shown state-of-the-art performance for skin segmentation [17]. Many stand-alone models based on different activation functions outperform ReLU: in this problem, the activation functions with maxInput = 1 work better than those initialized at 255; therefore, we set to 1 the maxInput for the ensembles with three models (FusAct3 and FusRan3).…”

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confidence: 99%

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Stochastic Selection of Activation Layers for Convolutional Neural Networks

Nanni

Lumini

Ghidoni

et al. 2020

Sensors

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In recent years, the field of deep learning has achieved considerable success in pattern recognition, image segmentation, and many other classification fields. There are many studies and practical applications of deep learning on images, video, or text classification. Activation functions play a crucial role in discriminative capabilities of the deep neural networks and the design of new “static” or “dynamic” activation functions is an active area of research. The main difference between “static” and “dynamic” functions is that the first class of activations considers all the neurons and layers as identical, while the second class learns parameters of the activation function independently for each layer or even each neuron. Although the “dynamic” activation functions perform better in some applications, the increased number of trainable parameters requires more computational time and can lead to overfitting. In this work, we propose a mixture of “static” and “dynamic” activation functions, which are stochastically selected at each layer. Our idea for model design is based on a method for changing some layers along the lines of different functional blocks of the best performing CNN models, with the aim of designing new models to be used as stand-alone networks or as a component of an ensemble. We propose to replace each activation layer of a CNN (usually a ReLU layer) by a different activation function stochastically drawn from a set of activation functions: in this way, the resulting CNN has a different set of activation function layers.

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“…In reference [ 12 ], an inception-based architecture was proposed that was composed of convolutional and inception modules with training considering both patches and whole images. In reference [ 13 ], the authors performed experiments using several CNN architectures and concluded that DeepLabv3+ is the best CNN for skin segmentation. Two very recent skin segmentation architectures are OR-Skip-Net [ 14 ], a model that transfers direct edge information across the network in such a way as to empower the features, and Skinny [ 15 ], based on a lightweight U-Net.…”

Section: Introductionmentioning

confidence: 99%

Postprocessing for Skin Detection

et al. 2021

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Skin detectors play a crucial role in many applications: face localization, person tracking, objectionable content screening, etc. Skin detection is a complicated process that involves not only the development of apposite classifiers but also many ancillary methods, including techniques for data preprocessing and postprocessing. In this paper, a new postprocessing method is described that learns to select whether an image needs the application of various morphological sequences or a homogeneity function. The type of postprocessing method selected is learned based on categorizing the image into one of eleven predetermined classes. The novel postprocessing method presented here is evaluated on ten datasets recommended for fair comparisons that represent many skin detection applications. The results show that the new approach enhances the performance of the base classifiers and previous works based only on learning the most appropriate morphological sequences.

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Fair comparison of skin detection approaches on publicly available datasets

Cited by 33 publications

References 52 publications

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Stochastic Selection of Activation Layers for Convolutional Neural Networks

Postprocessing for Skin Detection

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