Fusing CNNs and statistical indicators to improve image classification

Huertas‐Tato, Javier; Martín, Alfonso Jiménez; Fiérrez, Julián; Camacho, David

doi:10.1016/j.inffus.2021.09.012

Cited by 34 publications

(15 citation statements)

References 57 publications

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“…The rationale for using heterogeneous databases is that the fusion of heterogeneous data can provide additional information and increase the efficiency of neural network analysis and classification systems [ 46 ]. The use of heterogeneous data in training multimodal neural network systems will improve the accuracy of diagnostics by searching for connections between visual objects of research and statistical metadata [ 47 ].…”

Section: Methodsmentioning

confidence: 99%

System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of Heterogeneous Data Based on a Multimodal Neural Network

Lyakhov

Lyakhova

Nagornov

2022

Cancers

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Today, skin cancer is one of the most common malignant neoplasms in the human body. Diagnosis of pigmented lesions is challenging even for experienced dermatologists due to the wide range of morphological manifestations. Artificial intelligence technologies are capable of equaling and even surpassing the capabilities of a dermatologist in terms of efficiency. The main problem of implementing intellectual analysis systems is low accuracy. One of the possible ways to increase this indicator is using stages of preliminary processing of visual data and the use of heterogeneous data. The article proposes a multimodal neural network system for identifying pigmented skin lesions with a preliminary identification, and removing hair from dermatoscopic images. The novelty of the proposed system lies in the joint use of the stage of preliminary cleaning of hair structures and a multimodal neural network system for the analysis of heterogeneous data. The accuracy of pigmented skin lesions recognition in 10 diagnostically significant categories in the proposed system was 83.6%. The use of the proposed system by dermatologists as an auxiliary diagnostic method will minimize the impact of the human factor, assist in making medical decisions, and expand the possibilities of early detection of skin cancer.

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Section: Methodsmentioning

confidence: 99%

System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of Heterogeneous Data Based on a Multimodal Neural Network

Lyakhov

Lyakhova

Nagornov

2022

Cancers

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“…The CNN algorithm is an artificial neural network algorithm that is widely used in materials science, and it has also achieved outstanding performance in computer vision [45,46] and computer touch [47] as well as in the extraction of multiscale structure information in materials science. [48][49][50] In this article, the CNN model consisted of an input layer, four convolutional layers, and a fully connected layer.…”

Section: Cnn Modelmentioning

confidence: 99%

Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small‐Scale Dataset

Chai²,

Xiong

et al. 2022

Advanced Materials

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Deep‐learning (DL) methods, in consideration of their excellence in dealing with highly complex structure–performance relationships for materials, are expected to become a new design paradigm for breakthroughs in material performance. However, in most cases, it is impractical to collect massive‐scale experimental data or open‐source theoretical databases to support training DL models with sufficient prediction accuracy. In a dataset consisting of 483 porous silicone rubber observations generated via ink‐writing additive manufacturing, this work demonstrates that constructing low‐dimensional, accurate descriptors is the prerequisite for obtaining high‐precision DL models based on small experimental datasets. On this basis, a unique convolutional bidirectional long short‐term memory model with spatiotemporal features extraction capability is designed, whose hierarchical learning mechanism further reduces the requirement for the amount of data by taking full advantage of data information. The proposed approach can be expected as a powerful tool for innovative material design on small experimental datasets, which can also be used to explore the evolutionary mechanisms of the structures and properties of materials under complex working conditions.

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“…Machine learning has grown in popularity in the last decade, expanding from applications of models from a few available datasets to a wide range of scientific and technological fields. Deep learning (DL) is a subfield of machine learning which employs neural network structures that consist of an input layer, an output layer, and multiple hidden layers that can be range from two to tens or hundreds of layers with millions or even billions number of parameters, such as ResNetv2 and GPT-3 [1]. The ability to extract hidden useful knowledge…”

Section: Introductionmentioning

confidence: 99%

A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges

2022

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In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence, and it has been deployed in different fields of healthcare applications such as image processing, natural language processing, and signal processing. DL models have also been intensely used in different tasks of healthcare such as disease diagnostics and treatments. Deep learning techniques have surpassed other machine learning algorithms and proved to be the ultimate tools for many state-of-the-art applications. Despite all that success, classical deep learning has limitations and their models tend to be very confident about their predicted decisions because it does not know when it makes mistake. For the healthcare field, this limitation can have a negative impact on models predictions since almost all decisions regarding patients and diseases are sensitive. Therefore, Bayesian deep learning (BDL) has been developed to overcome these limitations. Unlike classical DL, BDL uses probability distributions for the model parameters, which makes it possible to estimate the whole uncertainties associated with the predicted outputs. In this regard, BDL offers a rigorous framework to quantify all sources of uncertainties in the model. This study reviews popular techniques of using Bayesian deep learning with their benefits and limitations. It also reviewed recent deep learning architecture such as Convolutional Neural Networks and Recurrent Neural Networks. In particular, the applications of Bayesian deep learning in healthcare have been discussed such as its use in medical imaging tasks, clinical signal processing, medical natural language processing, and electronic health records. Furthermore, this paper has covered the deployment of Bayesian deep learning for some of the widespread diseases. This paper has also discussed the fundamental research challenges and highlighted some research gaps in both the Bayesian deep learning and healthcare perspective.

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Fusing CNNs and statistical indicators to improve image classification

Cited by 34 publications

References 57 publications

System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of Heterogeneous Data Based on a Multimodal Neural Network

System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of Heterogeneous Data Based on a Multimodal Neural Network

Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small‐Scale Dataset

A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges

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