Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models

Singh, Dilbag; Taşpınar, Yavuz Selim; Kursun, Ramazan; Çinar, İ̇lkay; Köklü, Murat; Özkan, İlker Ali; Lee, Heung-No

doi:10.3390/electronics11070981

Cited by 49 publications

(21 citation statements)

References 35 publications

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“…In this paper, we assess classifiers with performance metrics: accuracy, sensitivity, specificity, precision, F1-Score, and G-mean [ 56 , 57 , 58 ]. Here,

: True Positive,

: False Positive,

: True Negative, and

: False Negative are presented in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

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A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet

Özaltın

Coskun²,

Yeniay

et al. 2022

Bioengineering

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A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. However, while doctors are analyzing each brain CT image, time is running fast. This circumstance may lead to result in a delay in treatment and making errors. Therefore, we targeted the utilization of an efficient artificial intelligence algorithm in stroke detection. In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. When we classified the dataset with OzNet, we acquired successful performance. However, for this target, we combined it with a minimum Redundancy Maximum Relevance (mRMR) method and Decision Tree (DT), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Support Vector Machines (SVM). In addition, 4096 significant features were obtained from the fully connected layer of OzNet, and we reduced the dimension of features from 4096 to 250 using the mRMR method. Finally, we utilized these machine learning algorithms to classify important features. As a result, OzNet-mRMR-NB was an excellent hybrid algorithm and achieved an accuracy of 98.42% and AUC of 0.99 to detect stroke from brain CT images.

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“…In this paper, we assess classifiers with performance metrics: accuracy, sensitivity, specificity, precision, F1-Score, and G-mean [ 56 , 57 , 58 ]. Here,

: True Positive,

: False Positive,

: True Negative, and

: False Negative are presented in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

“…In general, the AUC is also calculated to determine whether a particular condition exists regarding test data. If the AUC value is approximately 1, the classifier possesses very good performance [ 57 , 59 ]. In this paper, we also calculated AUC values for each classifier, and we demonstrated ROC curves.…”

Section: Resultsmentioning

confidence: 99%

A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet

Özaltın

Coskun²,

Yeniay

et al. 2022

Bioengineering

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show abstract

“…In order to objectively evaluate the performance of the methods, Accuracy (ACC), Sensitivity (TPR), Speci city (TNR), Precision (PRE), F1-Score, and Mathew Correlation Coe cient (MCC) metrics are calculated from the confusion matrix [11,[47][48][49][50]. In Figure 4, a multiclass confusion matrix is shown.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Bhamidipati

Yaşar

Taşpınar

et al. 2022

Computational Intelligence and Neuroscience

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Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. The classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. The results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance.

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“…One of the techniques used to evaluate the prediction performance of training and test data is the complexity matrix [32]. The values in the matrix are used to evaluate the results of classification problems and to compare their performance [33,34]. When a two-class confusion matrix given in Figure 4 is examined, the values of TP, TN, FP and FN given in the matrix mean the following;…”

Section: Confusion Matrixmentioning

confidence: 99%

Detection of accident situation by machine learning methods using traffic announcements: the case of metropol Istanbul

DAĞLI¹,

BÜBER²,

Taşpınar³

2022

International Journal of Applied Mathematics Electronics and Computers

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Information about the reality of the traffic accident, the clearness of the roads and the status of the accident can be obtained from the traffic accident announcements. By using the words in the radio or telephone announcements, you can be informed about the status of the accident. Inferences can be made with machine learning methods using a large number of data. In this study, the accident situation was classified using three different machine learning methods using radio and telephone announcements in Istanbul in Turkey. The dataset contains 156.856 announcement data. Classifications were performed using Artificial Neural Network (ANN), k-Nearest Neighbor (kNN) and Decision Tree (DT) machine learning methods. Classification success was 92.1% in the classification made with the ANN model, 91% in the classification made with the kNN model, and 89.8% in the classification made with the DT model. Classification performances of the models were also analyzed with precision, recall, F-1 Score and specificity metrics. In addition, the estimation abilities of the models with ROC curves and AUC values were analyzed. In addition, the training and testing times of the models were also analyzed. It will be possible to use the suggested models to automatically detect the accident situation from the announcements. In this way, it is thought that the most accurate direction can be made by obtaining information about crew orientation, traffic jams and the size of the accident.

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Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models

Cited by 49 publications

References 35 publications

A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet

A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Detection of accident situation by machine learning methods using traffic announcements: the case of metropol Istanbul

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