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The Diagnosis of Brucellosis in Rafsanjan City Using Deep Auto-Encoder Neural Networks
Hossein Ghayoumi Zadeh, Ali Fayazi, Mostafa Danaeian, Alae Saeidi,
Volume 6, Issue 4 (3-2020)
Abstract
Introduction: Brucellosis is considered as one of the most important common infectious diseases between humans and animals. Considering the endemic nature of brucellosis and the existence of numerous reports of human and animal cases of brucellosis in Iran, the incidence of human brucellosis in Rafsanjan city was determined in the last 3 years (2016–2018). The main objective of this study was to find an automated consistent and intelligent method with low sensitivity based on a neural network which is capable of accurate detection of brucellosis disease.
Methods: In this descriptive analytic study, cases of human brucellosis in Rafsanjan, south of Iran, were analyzed based on sex, age, pregnancy, history of contact with livestock and the use of non-pasteurized dairy products, Right Laboratory parameters and 2ME during 3 years (2016–2018). Data were split into two subsets of train (80%) and test (20%). The artificial neural network approach of the deep auto-encoder was used to train each subset.
Results: The deep auto-encoder method achieves 94.61% sensitivity, 90.84% accuracy and 50% specificity in the diagnosis of brucellosis over the experimental data sets. The experimental results also showed the excellent performance of the proposed artificial neural network.
Conclusion: The deep artificial neural network model can be used as an efficient and intelligent method to detect the human cases of brucellosis. However, further studies are required to design other models of artificial neural networks based on deep learning to detect other infectious diseases.

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.

Segmentation of the Left Atrial Appendage in the Echocardiographic Images of the Heart Using a Deep Neural Network
Hossein Ghayoumi Zadeh, Ali Fayazi, Khosro Rezaee, Mohammad Hossein Gholizadeh, Mehdi Eskandari,
Volume 8, Issue 3 (12-2021)
Abstract
Introduction: Cardiovascular diseases are one of the leading causes of mortality in today’s industrial world. Occlusion of left atrial appendage (LAA) using the manufactured devices is a growing trend. The objective of this study was to develop a computer-aided diagnosis system for the identification of LAA in echocardiographic images.
Method: The data used in this descriptive analytical study included 3D echocardiographic images of the heart of 32 patients in King’s College Hospital in London. All patients were treated successfully using the LAA closure device. A total of 208 two-dimensional images were first obtained from each 3D echocardiographic image data set. Then, 1914 images in which the LAA region was clearly recognizable were selected for this study. The proposed neural network was compiled based on the YOLOv3 algorithm. Finally, 1369 and 545 images were used for training and testing the algorithm, respectively.
Results: The performance of the algorithm in detecting the LAA on a set of 545 images was compared with the regions detected in similar images by an expert in echocardiography through intersection over union (IOU). The algorithm was able to correctly identify the LAA region in all 545 examined images with an average IOU of 99.37%.
Conclusion: The proposed image-based algorithm could detect LAA region in echocardiographic images with a high accuracy. This method can be used to develop algorithms for automatic analysis of the LAA region to determine the size of the closure device and to plan an efficient procedure in LAA occlusion methods.

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