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1.
Phys Rev Lett ; 126(4): 048101, 2021 Jan 29.
Artigo em Inglês | MEDLINE | ID: mdl-33576647

RESUMO

Recent advances in microscopy techniques make it possible to study the growth, dynamics, and response of complex biophysical systems at single-cell resolution, from bacterial communities to tissues and organoids. In contrast to ordered crystals, it is less obvious how one can reliably distinguish two amorphous yet structurally different cellular materials. Here, we introduce a topological earth mover's (TEM) distance between disordered structures that compares local graph neighborhoods of the microscopic cell-centroid networks. Leveraging structural information contained in the neighborhood motif distributions, the TEM metric allows an interpretable reconstruction of equilibrium and nonequilibrium phase spaces and embedded pathways from static system snapshots alone. Applied to cell-resolution imaging data, the framework recovers time ordering without prior knowledge about the underlying dynamics, revealing that fly wing development solves a topological optimal transport problem. Extending our topological analysis to bacterial swarms, we find a universal neighborhood size distribution consistent with a Tracy-Widom law.


Assuntos
Modelos Teóricos , Reconhecimento Automatizado de Padrão/métodos , Algoritmos , Animais , Fenômenos Biofísicos , Coloides/química , Microscopia Crioeletrônica , Drosophila , Entropia , Células Epiteliais/citologia , Interpretação de Imagem Assistida por Computador/métodos , Modelos Biológicos , Modelos Químicos , RNA/química
2.
Neural Netw ; 135: 158-176, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33388507

RESUMO

The sparse coding algorithm has served as a model for early processing in mammalian vision. It has been assumed that the brain uses sparse coding to exploit statistical properties of the sensory stream. We hypothesize that sparse coding discovers patterns from the data set, which can be used to estimate a set of stimulus parameters by simple readout. In this study, we chose a model of stereo vision to test our hypothesis. We used the Locally Competitive Algorithm (LCA), followed by a naïve Bayes classifier, to infer stereo disparity. From the results we report three observations. First, disparity inference was successful with this naturalistic processing pipeline. Second, an expanded, highly redundant representation is required to robustly identify the input patterns. Third, the inference error can be predicted from the number of active coefficients in the LCA representation. We conclude that sparse coding can generate a suitable general representation for subsequent inference tasks.


Assuntos
Algoritmos , Interpretação Estatística de Dados , Reconhecimento Automatizado de Padrão/métodos , Disparidade Visual/fisiologia , Percepção Visual/fisiologia , Teorema de Bayes , Humanos , Visão Ocular/fisiologia , Córtex Visual/fisiologia
3.
Neural Netw ; 135: 177-191, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33395588

RESUMO

This paper introduces a novel framework for generative models based on Restricted Kernel Machines (RKMs) with joint multi-view generation and uncorrelated feature learning, called Gen-RKM. To enable joint multi-view generation, this mechanism uses a shared representation of data from various views. Furthermore, the model has a primal and dual formulation to incorporate both kernel-based and (deep convolutional) neural network based models within the same setting. When using neural networks as explicit feature-maps, a novel training procedure is proposed, which jointly learns the features and shared subspace representation. The latent variables are given by the eigen-decomposition of the kernel matrix, where the mutual orthogonality of eigenvectors represents the learned uncorrelated features. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of generated samples on various standard datasets.


Assuntos
Aprendizado Profundo , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Estimulação Luminosa/métodos , Humanos
4.
Neural Netw ; 135: 201-211, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33401226

RESUMO

Discriminative learning based on convolutional neural networks (CNNs) aims to perform image restoration by learning from training examples of noisy-clean image pairs. It has become the go-to methodology for tackling image restoration and has outperformed the traditional non-local class of methods. However, the top-performing networks are generally composed of many convolutional layers and hundreds of neurons, with trainable parameters in excess of several million. We claim that this is due to the inherently linear nature of convolution-based transformation, which is inadequate for handling severe restoration problems. Recently, a non-linear generalization of CNNs, called the operational neural networks (ONN), has been shown to outperform CNN on AWGN denoising. However, its formulation is burdened by a fixed collection of well-known non-linear operators and an exhaustive search to find the best possible configuration for a given architecture, whose efficacy is further limited by a fixed output layer operator assignment. In this study, we leverage the Taylor series-based function approximation to propose a self-organizing variant of ONNs, Self-ONNs, for image restoration, which synthesizes novel nodal transformations on-the-fly as part of the learning process, thus eliminating the need for redundant training runs for operator search. In addition, it enables a finer level of operator heterogeneity by diversifying individual connections of the receptive fields and weights. We perform a series of extensive ablation experiments across three severe image restoration tasks. Even when a strict equivalence of learnable parameters is imposed, Self-ONNs surpass CNNs by a considerable margin across all problems, improving the generalization performance by up to 3 dB in terms of PSNR.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Humanos , Neurônios/fisiologia , Estimulação Luminosa/métodos
5.
Neural Netw ; 135: 1-12, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33310193

RESUMO

Knowledge graph reasoning aims to find reasoning paths for relations over incomplete knowledge graphs (KG). Prior works may not take into account that the rewards for each position (vertex in the graph) may be different. We propose the distance-aware reward in the reinforcement learning framework to assign different rewards for different positions. We observe that KG embeddings are learned from independent triples and therefore cannot fully cover the information described in the local neighborhood. To this effect, we integrate a graph self-attention (GSA) mechanism to capture more comprehensive entity information from the neighboring entities and relations. To let the model remember the path, we incorporate the GSA mechanism with GRU to consider the memory of relations in the path. Our approach can train the agent in one-pass, thus eliminating the pre-training or fine-tuning process, which significantly reduces the problem complexity. Experimental results demonstrate the effectiveness of our method. We found that our model can mine more balanced paths for each relation.


Assuntos
Bases de Dados Factuais , Aprendizado Profundo , Reconhecimento Automatizado de Padrão/métodos , Reforço Psicológico , Algoritmos , Bases de Dados Factuais/tendências , Aprendizado Profundo/tendências , Humanos , Conhecimento , Reconhecimento Automatizado de Padrão/tendências
6.
Neural Netw ; 135: 68-77, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33360149

RESUMO

The goal of n-shot learning is the classification of input data from small datasets. This type of learning is challenging in neural networks, which typically need a high number of data during the training process. Recent advancements in data augmentation allow us to produce an infinite number of target conditions from the primary condition. This process includes two main steps for finding the best augmentations and training the data with the new augmentation techniques. Optimizing these two steps for n-shot learning is still an open problem. In this paper, we propose a new method for auto-augmentation to address both of these problems. The proposed method can potentially extract many possible types of information from a small number of available data points in n-shot learning. The results of our experiments on five prominent n-shot learning datasets show the effectiveness of the proposed method.


Assuntos
Bases de Dados Factuais , Aprendizado Profundo , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Estimulação Luminosa/métodos , Bases de Dados Factuais/tendências , Aprendizado Profundo/tendências , Humanos , Reconhecimento Automatizado de Padrão/tendências
7.
Neural Netw ; 133: 69-86, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33125919

RESUMO

The data imbalance problem in classification is a frequent but challenging task. In real-world datasets, numerous class distributions are imbalanced and the classification result under such condition reveals extreme bias in the majority data class. Recently, the potential of GAN as a data augmentation method on minority data has been studied. In this paper, we propose a classification enhancement generative adversarial networks (CEGAN) to enhance the quality of generated synthetic minority data and more importantly, to improve the prediction accuracy in data imbalanced condition. In addition, we propose an ambiguity reduction method using the generated synthetic minority data for the case of multiple similar classes that are degenerating the classification accuracy. The proposed method is demonstrated with five benchmark datasets. The results indicate that approximating the real data distribution using CEGAN improves the classification performance significantly in data imbalanced conditions compared with various standard data augmentation methods.


Assuntos
Análise de Dados , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/classificação , Reconhecimento Automatizado de Padrão/métodos , Humanos
8.
Neural Netw ; 133: 112-122, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33181405

RESUMO

Transfer learning enables solving a specific task having limited data by using the pre-trained deep networks trained on large-scale datasets. Typically, while transferring the learned knowledge from source task to the target task, the last few layers are fine-tuned (re-trained) over the target dataset. However, these layers are originally designed for the source task that might not be suitable for the target task. In this paper, we introduce a mechanism for automatically tuning the Convolutional Neural Networks (CNN) for improved transfer learning. The pre-trained CNN layers are tuned with the knowledge from target data using Bayesian Optimization. First, we train the final layer of the base CNN model by replacing the number of neurons in the softmax layer with the number of classes involved in the target task. Next, the CNN is tuned automatically by observing the classification performance on the validation data (greedy criteria). To evaluate the performance of the proposed method, experiments are conducted on three benchmark datasets, e.g., CalTech-101, CalTech-256, and Stanford Dogs. The classification results obtained through the proposed AutoTune method outperforms the standard baseline transfer learning methods over the three datasets by achieving 95.92%, 86.54%, and 84.67% accuracy over CalTech-101, CalTech-256, and Stanford Dogs, respectively. The experimental results obtained in this study depict that tuning of the pre-trained CNN layers with the knowledge from the target dataset confesses better transfer learning ability. The source codes are available at https://github.com/JekyllAndHyde8999/AutoTune_CNN_TransferLearning.


Assuntos
Bases de Dados Factuais , Aprendizado de Máquina , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Animais , Teorema de Bayes , Cães
9.
Neural Netw ; 133: 148-156, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33217683

RESUMO

Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability. Despite many training strategies proposed to improve training stability, this issue remains as a challenge. In this paper, we investigate the training instability from the perspective of adversarial samples and reveal that adversarial training on fake samples is implemented in vanilla GANs, but adversarial training on real samples has long been overlooked. Consequently, the discriminator is extremely vulnerable to adversarial perturbation and the gradient given by the discriminator contains non-informative adversarial noises, which hinders the generator from catching the pattern of real samples. Here, we develop adversarial symmetric GANs (AS-GANs) that incorporate adversarial training of the discriminator on real samples into vanilla GANs, making adversarial training symmetrical. The discriminator is therefore more robust and provides more informative gradient with less adversarial noise, thereby stabilizing training and accelerating convergence. The effectiveness of the AS-GANs is verified on image generation on CIFAR-10, CIFAR-100, CelebA, and LSUN with varied network architectures. Not only the training is more stabilized, but the FID scores of generated samples are consistently improved by a large margin compared to the baseline. Theoretical analysis is also conducted to explain why AS-GAN can improve training. The bridging of adversarial samples and adversarial networks provides a new approach to further develop adversarial networks.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Aprendizado de Máquina não Supervisionado , Humanos , Distribuição Normal
10.
Ultrasonics ; 110: 106268, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33068826

RESUMO

The segmentation of cancer-suspicious skin lesions using ultrasound may help their differential diagnosis and treatment planning. Active contour models (ACM) require an initial seed, which when manually chosen may cause variations in segmentation accuracy. Fully-automated skin segmentation typically employs layer-by-layer segmentation using a combination of methods; however, such segmentation has not yet been applied on cancerous lesions. In the current work, fully automated segmentation is achieved in two steps: an automated seeding (AS) step using a layer-by-layer method followed by a growing step using an ACM. The method was tested on images of nevi, melanomas, and basal cell carcinomas from two ultrasound imaging systems (N=60), with all lesions being successfully located. For the seeding step, manual seeding (MS) was used as a reference. AS approached the accuracy of MS when the latter used an optimal bounding rectangle based on the ground truth (Sørensen-Dice coefficient (SDC) of 72.3 vs 74.6, respectively). The effect of varying the manual seed was also investigated; a 0.7 decrease in seed height and width caused a mean SDC of 54.6. The results show the robustness of automated seeding for skin lesion segmentation.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Reconhecimento Automatizado de Padrão/métodos , Neoplasias Cutâneas/diagnóstico por imagem , Ultrassonografia/métodos , Diagnóstico Diferencial , Humanos
11.
Ultrasonics ; 110: 106271, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33166786

RESUMO

Accurate breast mass segmentation of automated breast ultrasound (ABUS) is a great help to breast cancer diagnosis and treatment. However, the lack of clear boundary and significant variation in mass shapes make the automatic segmentation very challenging. In this paper, a novel automatic tumor segmentation method SC-FCN-BLSTM is proposed by incorporating bi-directional long short-term memory (BLSTM) and spatial-channel attention (SC-attention) module into fully convolutional network (FCN). In order to decrease performance degradation caused by ambiguous boundaries and varying tumor sizes, an SC-attention module is designed to integrate both finer-grained spatial information and rich semantic information. Since ABUS is three-dimensional data, utilizing inter-slice context can improve segmentation performance. A BLSTM module with SC-attention is constructed to model the correlation between slices, which employs inter-slice context to assist segmentation for false positive elimination. The proposed method is verified on our private ABUS dataset of 124 patients with 170 volumes, including 3636 2D labeled slices. The Dice similarity coefficient (DSC), Recall, Precision and Hausdorff distance (HD) of the proposed method are 0.8178, 0.8067, 0.8292 and 11.1367. Experimental results demonstrate that the proposed method offered improved segmentation results compared with existing deep learning-based methods.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Ultrassonografia Mamária/métodos , Algoritmos , Diagnóstico por Computador , Feminino , Humanos
12.
Ultrasonics ; 111: 106326, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33348233

RESUMO

Biometric recognition systems based on ultrasonic images have several advantages over other technologies, including the capability of capturing 3D images and detecting liveness. In this work, a recognition system based on hand geometry achieved through ultrasound images is proposed and experimentally evaluated. 3D images of human hand are acquired by performing parallel mechanical scans with a commercial ultrasound probe. Several 2D images are then extracted at increasing under-skin depths and, from each of them, up to 26 distances among key points of the hand are defined and computed to achieve a 2D template. A 3D template is then obtained by combining in several ways 2D templates of two or more images. A preliminary evaluation of the system is achieved by carrying out verification experiments on a home-made database. Results have shown a good recognition accuracy: the Equal Error Rate was 1.15% when a single 2D image is used and improved to 0.98% by using the 3D template. The possibility to upgrade the proposed system to a multimodal system, by extracting from the same volume other features like palmprint and hand veins, as well as possible improvements are finally discussed.


Assuntos
Biometria/métodos , Mãos/diagnóstico por imagem , Imageamento Tridimensional/métodos , Ultrassonografia/métodos , Algoritmos , Humanos , Processamento de Imagem Assistida por Computador/métodos , Reconhecimento Automatizado de Padrão/métodos
13.
Neural Netw ; 133: 220-228, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33232858

RESUMO

Attribution editing has achieved remarkable progress in recent years owing to the encoder-decoder structure and generative adversarial network (GAN). However, it remains challenging to generate high-quality images with accurate attribute transformation. Attacking these problems, the work proposes a novel selective attribute editing model based on classification adversarial network (referred to as ClsGAN) that shows good balance between attribute transfer accuracy and photo-realistic images. Considering that the editing images are prone to be affected by original attribute due to skip-connection in encoder-decoder structure, an upper convolution residual network (referred to as Tr-resnet) is presented to selectively extract information from the source image and target label. In addition, to further improve the transfer accuracy of generated images, an attribute adversarial classifier (referred to as Atta-cls) is introduced to guide the generator from the perspective of attribute through learning the defects of attribute transfer images. Experimental results on CelebA demonstrate that our ClsGAN performs favorably against state-of-the-art approaches in image quality and transfer accuracy. Moreover, ablation studies are also designed to verify the great performance of Tr-resnet and Atta-cls.


Assuntos
Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/classificação , Humanos , Reconhecimento Automatizado de Padrão/métodos
15.
PLoS One ; 15(10): e0237570, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33044975

RESUMO

Photo-identification (photo-id) is a method used in field studies by biologists to monitor animals according to their density, movement patterns and behavior, with the aim of predicting and preventing ecological risks. However, these methods can introduce subjectivity when manually classifying an individual animal, creating uncertainty or inaccuracy in the data as a result of the human criteria involved. One of the main objectives in photo-id is to implement an automated mechanism that is free of biases, portable, and easy to use. The main aim of this work is to develop an autonomous and portable photo-id system through the optimization of image classification algorithms that have high statistical dependence, with the goal of classifying dorsal fin images of the blue whale through offline information processing on a mobile platform. The new proposed methodology is based on the Scale Invariant Feature Transform (SIFT) that, in conjunction with statistical discriminators such as the variance and the standard deviation, fits the extracted data and selects the closest pixels that comprise the edges of the dorsal fin of the blue whale. In this way, we ensure the elimination of the most common external factors that could affect the quality of the image, thus avoiding the elimination of relevant sections of the dorsal fin. The photo-id method presented in this work has been developed using blue whale images collected off the coast of Baja California Sur. The results shown have qualitatively and quantitatively validated the method in terms of its sensitivity, specificity and accuracy on the Jetson Tegra TK1 mobile platform. The solution optimizes classic SIFT, balancing the results obtained with the computational cost, provides a more economical form of processing and obtains a portable system that could be beneficial for field studies through mobile platforms, making it available to scientists, government and the general public.


Assuntos
Nadadeiras de Animais/anatomia & histologia , Balaenoptera/anatomia & histologia , Aplicativos Móveis , Fotografação/métodos , Algoritmos , Animais , Bases de Dados Factuais , Processamento de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Reconhecimento Automatizado de Padrão/métodos , Reconhecimento Automatizado de Padrão/estatística & dados numéricos , Fotografação/estatística & dados numéricos
16.
PLoS One ; 15(9): e0238302, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32886692

RESUMO

Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, deployed classifiers often require the ability to recognize inputs from outside the training set as unknowns. This problem has been studied under multiple paradigms including out-of-distribution detection and open set recognition. For convolutional neural networks, there have been two major approaches: 1) inference methods to separate knowns from unknowns and 2) feature space regularization strategies to improve model robustness to novel inputs. Up to this point, there has been little attention to exploring the relationship between the two approaches and directly comparing performance on large-scale datasets that have more than a few dozen categories. Using the ImageNet ILSVRC-2012 large-scale classification dataset, we identify novel combinations of regularization and specialized inference methods that perform best across multiple open set classification problems of increasing difficulty level. We find that input perturbation and temperature scaling yield significantly better performance on large-scale datasets than other inference methods tested, regardless of the feature space regularization strategy. Conversely, we find that improving performance with advanced regularization schemes during training yields better performance when baseline inference techniques are used; however, when advanced inference methods are used to detect open set classes, the utility of these combersome training paradigms is less evident.


Assuntos
Algoritmos , Classificação/métodos , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Conjuntos de Dados como Assunto , Humanos , Aprendizado de Máquina
17.
PLoS Comput Biol ; 16(9): e1007758, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32881897

RESUMO

With the ever-increasing quality and quantity of imaging data in biomedical research comes the demand for computational methodologies that enable efficient and reliable automated extraction of the quantitative information contained within these images. One of the challenges in providing such methodology is the need for tailoring algorithms to the specifics of the data, limiting their areas of application. Here we present a broadly applicable approach to quantification and classification of complex shapes and patterns in biological or other multi-component formations. This approach integrates the mapping of all shape boundaries within an image onto a global information-rich graph and machine learning on the multidimensional measures of the graph. We demonstrated the power of this method by (1) extracting subtle structural differences from visually indistinguishable images in our phenotype rescue experiments using the endothelial tube formations assay, (2) training the algorithm to identify biophysical parameters underlying the formation of different multicellular networks in our simulation model of collective cell behavior, and (3) analyzing the response of U2OS cell cultures to a broad array of small molecule perturbations.


Assuntos
Biologia Computacional/métodos , Processamento de Imagem Assistida por Computador/métodos , Aprendizado de Máquina , Reconhecimento Automatizado de Padrão/métodos , Algoritmos , Linhagem Celular Tumoral , Técnicas Citológicas , Árvores de Decisões , Técnicas de Silenciamento de Genes , Células Endoteliais da Veia Umbilical Humana , Humanos
18.
PLoS One ; 15(9): e0238423, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32877456

RESUMO

In this paper we introduce a real Pashtu handwritten numerals dataset (PHND) having 50,000 scanned images and make publicly available for research and scientific use. Although more than fifty million people in the world use this language for written and oral communication, no significant efforts are devoted to the Pashtu Optical Character Recognition (POCR). We present a new approach for Pahstu handwritten numerals recognition (PHNR) based on deep neural networks. We train Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) on high-frequency numerals for feature extraction and classification. We evaluated the performance of the proposed algorithm on the newly introduced Pashtu handwritten numerals database PHND and Bangla language number database CMATERDB 3.1.1. We obtained best recognition rate of 98.00% and 98.64% on PHND and CMATERDB 3.1.1. respectively.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Reconhecimento Automatizado de Padrão/métodos , Redação/normas , Adulto , Idoso , Algoritmos , Aprendizado Profundo , Feminino , Escrita Manual , Humanos , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação
19.
PLoS One ; 15(8): e0237009, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32780738

RESUMO

In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set. However, generalization of such an algorithm and thus achieving a similar classification performance is only possible when the training data used to build the algorithm is similar to new unseen data one wishes to apply it to. It is often unknown in advance how an algorithm will perform on new unseen data, being a crucial reason for not deploying an algorithm at all. Therefore, tools are needed to measure the similarity of data sets. In this paper, we propose the Data Representativeness Criterion (DRC) to determine how representative a training data set is of a new unseen data set. We present a proof of principle, to see whether the DRC can quantify the similarity of data sets and whether the DRC relates to the performance of a supervised classification algorithm. We compared a number of magnetic resonance imaging (MRI) data sets, ranging from subtle to severe difference is acquisition parameters. Results indicate that, based on the similarity of data sets, the DRC is able to give an indication as to when the performance of a supervised classifier decreases. The strictness of the DRC can be set by the user, depending on what one considers to be an acceptable underperformance.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Reconhecimento Automatizado de Padrão/métodos , Algoritmos , Análise de Dados , Interpretação Estatística de Dados , Humanos , Imagem por Ressonância Magnética , Estudo de Prova de Conceito , Aprendizado de Máquina Supervisionado
20.
PLoS One ; 15(8): e0229367, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32790672

RESUMO

Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Previsões/métodos , Algoritmos , Teorema de Bayes , Feminino , Humanos , Aprendizado de Máquina , Imageamento de Micro-Ondas , Modelos Teóricos , Redes Neurais de Computação , Reconhecimento Automatizado de Padrão/métodos , Máquina de Vetores de Suporte
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