Convolution neural network joint with mixture of extreme learning machines for feature extraction and classification of accident images

Pashaei, Ali; Ghatee, Mehdi; Sajedi, Hedieh

doi:10.1007/s11554-019-00852-3

Cited by 44 publications

(24 citation statements)

References 49 publications

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“…Phaye et al [ 47 ] developed a deep learning model for the feature extraction and mixture of experts for classification. For the first step, the outputs of the last max-pooling layer of a convolution neural network (CNN) are utilized to extract the hidden features automatically.…”

Section: Discussionmentioning

confidence: 99%

“…The differential deep-CNN model performance was evaluated with twelve previous models, such as [ 5 , 15 , 21 , 23 , 37 , 38 , 39 , 40 , 41 , 47 , 48 , 49 ]. Through observing the aforementioned experimental results, the performance of the proposed technique is better as compared to previous approaches, which demonstrate the applicability of the proposed model.…”

Section: Discussionmentioning

confidence: 99%

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Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

et al. 2021

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The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

et al. 2021

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“…Despite not having the same power as the conventional CNNs (with fully connected layers and backpropagation) to extract features, CELM's accuracy proved competitive in the analyzed scenarios and benchmark datasets. The competitiveness of the results is clear when, in many cases, CELM was superior to several traditional models such as MLP (as in [102], [69], [50]) e SVM (as in [46], [113], [90]). Observing these results, we reported a good generalization and good representativeness by CELM [104], [68], [97], [27], [55], [57], [49].…”

Section: Rq 3: Which Are the Main Findings When Applying Celm In Problems Based On Image Analysis?mentioning

confidence: 99%

“…Different from the previous work, other authors propose their architecture instead of using a known network for the transfer learning, such as [49], [46], [64], [102], [103], [116] that propose CELM architectures with a different number of convolutional and pooling layers. The authors use CNN architectures for training the data with the fully connected layers.…”

Section: Pre-trained Cnn In Same Application Domain For Feature Extraction and Elm For Fast Learningmentioning

confidence: 99%

Convolutional Extreme Learning Machines: A Systematic Review

Rodrigues¹,

Rogério²,

Kelner³

et al. 2021

Preprint

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Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

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“…[20] Since crashes are directly related to the human lives, the artificial neural network will have widespread application in making major decisions including prediction of the type and severity of collisions and proposing alternatives in order to reduce it, without the requirement for any predefined assumptions and relations, and with higher accuracy than statistical methods [21,22]. Nonlinear relationship between variables can be modelled with various types of ANN in order to recognize the effect of influential factors in an event occurred and predict the future events [23][24][25][26]. Chang utilized two models of artificial neural network and negative binomial regression for analyzing and modeling road crashes.…”

Section: Previous Studiesmentioning

confidence: 99%

Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

2021

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The purpose of this study is to investigate and determine the factors affecting vehicle and pedestrian accidents taking place in the busiest suburban highway of Guilan Province located in the north of Iran and provide the most accurate prediction model. Therefore, the effective principal variables and the probability of occurrence of each category of crashes are analyzed and computed utilizing the factor analysis, logit, and Machine Learning approaches simultaneously. This method not only could contribute to achieving the most comprehensive and efficient model to specify the major contributing factor, but also it can provide officials with suggestions to take effective measures with higher precision to lessen accident impacts and improve road safety. Both the factor analysis and logit model show the significant roles of exceeding lawful speed, rainy weather and driver age (30–50) variables in the severity of vehicle accidents. On the other hand, the rainy weather and lighting condition variables as the most contributing factors in pedestrian accidents severity, underline the dominant role of environmental factors in the severity of all vehicle-pedestrian accidents. Moreover, considering both utilized methods, the machine-learning model has higher predictive power in all cases, especially in pedestrian accidents, with 41.6% increase in the predictive power of fatal accidents and 12.4% in whole accidents. Thus, the Artificial Neural Network model is chosen as the superior approach in predicting the number and severity of crashes. Besides, the good performance and validation of the machine learning is proved through performance and sensitivity analysis.

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Convolution neural network joint with mixture of extreme learning machines for feature extraction and classification of accident images

Cited by 44 publications

References 49 publications

Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

Convolutional Extreme Learning Machines: A Systematic Review

Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

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