Uso de Redes Neurais Convolucionais para Identificar Displasia Cortical Focal em Pacientes com Epilepsia Refratária

Silva, Samuel; Simozo, Fabricio; Murta, Leonardo; Tinós, Renato

doi:10.5753/eniac.2020.12130

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…A methodology for generating the inputs of the CNN similar to that adopted in [15] is used, in which each image is divided into small rectangular windows. Here, the objective is to classify each of the image windows into two classes: normal and osteosarcoma.…”

Section: A Dataset and Pre-processingmentioning

confidence: 99%

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Detection of Osteosarcoma on Bone Radiographs Using Convolutional Neural Networks

Asito¹,

Pereira²,

Nogueira-Barbosa³

et al. 2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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We propose a computer-aided diagnosis system based on convolutional neural networks (CNNs) for the identification of osteosarcoma on bone radiographs. The CNN should indicate regions of the image that may contain tumors. In order to indicate these regions on the image, we propose to split the image in windows and individually classify them by using a CNN. Techniques for pre-processing, such as window exclusion and labeling, are proposed. Two CNNs are compared in the proposed system. The first one is trained from scratch, while the second one is a pre-trained CNN (VGG16). The CNNs are compared to four machine learning models that use features extracted from the image windows as inputs: multilayer perceptron (MLP), decision tree, random forest, and MLP with feature selection. In the experiments, the best performance was obtained by the pre-trained CNN.

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Section: A Dataset and Pre-processingmentioning

confidence: 99%

“…Here, the objective is to classify each of the image windows into two classes: normal and osteosarcoma. In [15], the objective was to classify Focal Cortical Dysplasia in windows of brain images obtained by Magnetic Resonance Imaging. Procedures were developed for automatically creating and labeling the image windows.…”

Section: A Dataset and Pre-processingmentioning

confidence: 99%

Detection of Osteosarcoma on Bone Radiographs Using Convolutional Neural Networks

Asito¹,

Pereira²,

Nogueira-Barbosa³

et al. 2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

Self Cite

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“…No trabalho anterior a esse(SILVA, 2019) foi constatado um possível viés no resultado final apresentado. No projeto as imagens da base foram divididas em janelas, sendo a escolha do tamanho das janelas pré-definida.…”

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Uso de algoritmos genéticos e redes convolucionais para classificação de displasias corticais focais em pacientes com epilepsia refratária

Silva

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Focal Cortical Dysplasia (FCD) is a type of brain injury that is the main cause of Refractory Epilepsy in children, and the third largest cause in adults, present in more than 50% of childhood cases and 20% in adult cases. Patients with this inflammatory disease of epileptic seizures where the drugs are not able to help. The most appropriate clinical treatment in this case is surgery. But to be performed it is necessary if you have an accurate identification of the injury. This process is quite complex because the region of the lesion is not well defined for viewing on Magnetic Resonance Images (MRI). However, with the computational advances, have been suggested several techniques to aid in the classification of medical images. In this work, the use of Genetic Algorithm with Convolutional Neural Networks (CNN) to classify images with the presence of FCD is studied. The CNN-based system should detect and identify the location of FCD in the images. For this, it is proposed that CNN classify rectangular windows of the images in order to locate regions affected by FCD. The size of the windows and the overlap between them has a direct impact on the efficiency of the CNN-based system. In addition, hyper-parameters strongly influence CNN's performance. It is proposed here, using a genetic algorithm to: i) define the size of the windows; ii) define the overlap of the windows; iii) define some of CNN's hyper-parameters. As a result, the AG proved to be quite beneficial, optimizing the CNN in order to obtain an accuracy greater than 90% in the classification

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Histopathology Osteosarcoma Image Classification

Chhoker

Saxena

Rai

et al. 2023

Lecture Notes in Networks and Systems

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Uso de Redes Neurais Convolucionais para Identificar Displasia Cortical Focal em Pacientes com Epilepsia Refratária

Cited by 3 publications

References 11 publications

Detection of Osteosarcoma on Bone Radiographs Using Convolutional Neural Networks

Detection of Osteosarcoma on Bone Radiographs Using Convolutional Neural Networks

Uso de algoritmos genéticos e redes convolucionais para classificação de displasias corticais focais em pacientes com epilepsia refratária

Histopathology Osteosarcoma Image Classification

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