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Showing 3 results for Feature Extraction
Extraction of Suitable Features for Breast Cancer Detection Using Dynamic Analysis of Thermographic Images
Hossein Ghayoumi Zadeh , Ali Fayazi, Bita Binazir, Mostafa Yargholi,
Volume 7, Issue 2 (9-2020)
Abstract
Introduction: Thermography is a non-invasive imaging technique that can be used to diagnose breast cancer. In this study, a method was presented for the extraction of suitable features in dynamic thermographic images of breast. The extracted features can help classify thermographic images as cancerous or healthy.
Method: In this descriptive-analytical study, the images were taken from the IC/UFF database. A total of 196 people, including 41 cancer patients and 155 healthy individuals were investigated. Each person had 10 thermographic images and in total, 1960 images were analyzed. The images were captured using the FLIR ThermaCam S45 camera. The proposed model was presented based on a series of breast thermographic images of an individual to extract 8 suitable features.  The extracted features included mean, standard deviation, entropy, kurtosis, homogeneity, energy, skewness, and variance.
Results: The extracted features were evaluated by the classifiers including the decision tree, support vector machine, quadratic symmetric analysis, and K-nearest neighbor algorithm using the ten-fold cross validation. The accuracy and sensitivity were 99% and 99.33% for decision tree algorithm, 98.46% and 95.12% for support vector machine algorithm, 100% and 100%, and 99% and 97.56% for K-nearest neighbor algorithm.
Conclusion: The results of this study showed that among the first-order statistical features, mean difference, skewness, entropy, and standard deviation are the most effective features which help to detect asymmetry. The features extracted by the proposed model can help classify the individuals into healthy or cancer-affected by thermal images.

Detection and Classification of Emotions Using Physiological Signals and Pattern Recognition Methods
Naser Safdarian, Mohsen Naji,
Volume 7, Issue 2 (9-2020)
Abstract
Introduction: Emotions play an important role in health, communication, and interaction between humans. The ability to recognize the emotional status of people is an important indicator of health and natural relationships. In DEAP database, electroencephalogram (EEG) signals as well as environmental physiological signals related to 32 volunteers are registered. The participants in each video were rated in terms of level of arousal, capacity, liking/disliking, proficiency, and familiarity with the video they watched.
Method: In this study, a practical empirical method was adopted to classify capacity, arousal, proficiency, and interest by ranking the features extracted from signals using algorithms on EEG signals and environmental physiological signals (such as electromyography (EMG), electrooculography (EOG), galvanic skin response (GSR), respiration rate, photoplethysmography (PPG), and skin temperature. After initializing the signals from the database and pre-processing them, various features in the time and frequency domain were extracted from all signals. In this study, SVM and KNN classifiers, K-means clustering algorithm, and neural networks, such as PNN and GRNN were used to identify and classify emotions.
Results: It was indicated in this study that the results of the classification of emotions using various methods and classifiers were well-established with high accuracy. The best accuracy results were obtained by applying the proposed method using SVM classifier based on features extracted from environmental signals (85.5%) and EEG signals (82.4%).
Conclusion: According to the results of the classification of emotions in this study, the proposed algorithm provides relatively better results compared with previous similar methods.

Diagnosis of COVID-19 Disease in X-ray Images Based on Deep Learning Methods and Combining Classifiers
Mohammad Roustaei , Davar Giveki,
Volume 10, Issue 2 (9-2023)
Abstract
Introduction: COVID-19 is a new virus that causes infection in the upper respiratory tract and lungs, and the number of deaths due to the disease has increased daily on the scale of a global epidemic. Chest X-ray images have been useful for monitoring various lung diseases and have recently been used to monitor COVID-19 disease.
Method: In this research, a multi-stage process was used to recognize COVID-19 from X-ray images. In the first stage, pre-processing was done to normalize the data. In the second step, which is the most important step of the proposed method, feature extraction was done. The feature extraction operation was based on deep learning networks. After feature extraction, machine learning algorithms were used to classify images. The algorithms used in this section are support vector machine, nearest neighbor, and decision tree algorithms. The results of these categories are combined in the fourth step based on the majority vote.
Results: The parameters used in this research are among the classification parameters, including precision, accuracy, recall, and F-criterion, which were obtained as 96.5, 92.25, 94, and 93, respectively.
Conclusion: The results of the experiments show the acceptable efficiency of the proposed method because, in addition to reducing the calculations by the separable layer, the combination of categories and their weighting has been used to obtain the final result.

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