An automatic recognition of glaucoma in fundus images using deep learning and random forest classifier

2021 ◽  
pp. 107512
Author(s):  
Shanmugam P. ◽  
Raja J. ◽  
Pitchai R.
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Automatic Recognition ◽  
Random Forest Classifier ◽  
Fundus Images

scholarly journals ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3883
Author(s):  
Muhammad Kashif Yaqoob ◽  
Syed Farooq Ali ◽  
Muhammad Bilal ◽  
Muhammad Shehzad Hanif ◽  
Ubaid M. Al-Saggaf
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Random Forest ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
State Of The Art ◽  
Random Forest Classifier ◽  
Eye Disease ◽  
Diabetic Patients ◽  
The Right

Diabetic retinopathy, an eye disease commonly afflicting diabetic patients, can result in loss of vision if prompt detection and treatment are not done in the early stages. Once the symptoms are identified, the severity level of the disease needs to be classified for prescribing the right medicine. This study proposes a deep learning-based approach, for the classification and grading of diabetic retinopathy images. The proposed approach uses the feature map of ResNet-50 and passes it to Random Forest for classification. The proposed approach is compared with five state-of-the-art approaches using two category Messidor-2 and five category EyePACS datasets. These two categories on the Messidor-2 dataset include ’No Referable Diabetic Macular Edema Grade (DME)’ and ’Referable DME’ while five categories consist of ‘Proliferative diabetic retinopathy’, ‘Severe’, ‘Moderate’, ‘Mild’, and ‘No diabetic retinopathy’. The results show that the proposed approach outperforms compared approaches and achieves an accuracy of 96% and 75.09% for these datasets, respectively. The proposed approach outperforms six existing state-of-the-art architectures, namely ResNet-50, VGG-19, Inception-v3, MobileNet, Xception, and VGG16.

scholarly journals Genetic Algorithm Based Hybrid Model Of convolutional Neural Network And Random Forest Classifier For Sentiment Classification

2021 ◽  
Vol 12 (2) ◽  
pp. 3216-3223
Author(s):  
Siji George C G, Et. al.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Random Forest Classifier ◽  
Sentiment Classification ◽  
Experimental Result ◽  
Text Input

Sentiment analysis is one of the active research areas in the field of datamining. Machine learning algorithms are capable to implement sentiment analysis. Due to the capacity of self-learning and massive data handling, most of the researchers are using deep learning neural networks for solving sentiment classification tasks. So, in this paper, a new model is designed under a hybrid framework of machine learning and deep learning which couples Convolutional Neural Network and Random Forest classifier for fine-grained sentiment analysis. The Continuous Bag-of-Word (CBOW) model is used to vectorize the text input. The most important features are extracted by the Convolutional Neural Network (CNN). The extracted features are used by the Random Forest(RF) classifier for sentiment classification. The performance of the proposed hybrid CNNRF model is comparedwith the base model such as Convolutional Neural Network (CNN) and Random Forest (RF) classifier. The experimental result shows that the proposed model far beat the existing base models in terms of classification accuracy and effectively integrated genetically-modified CNN with Random Forest classifier.

The Performance Improvement on 4-FB Face using Random Forest Classifier

2018 ◽  
Vol 10 (5) ◽  
pp. 1-12
Author(s):  
B. Nassih ◽  
A. Amine ◽  
M. Ngadi ◽  
D. Naji ◽  
N. Hmina
Keyword(s):  
Random Forest ◽  
Performance Improvement ◽  
Random Forest Classifier

Methodology for Malware Classification using a Random Forest Classifier

2018 ◽  
Author(s):  
Carlos Domenick Morales-Molina ◽  
Diego Santamaria-Guerrero ◽  
Gabriel Sanchez-Perez ◽  
Hector Perez-Meana ◽  
Aldo Hernandez-Suarez
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Malware Classification

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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