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In this study, a new deep learning-based approach has been developed that detects and classifies surface defects that occur in the steel production process. The proposed methodology was created in four steps. In the first step, a deep learning model is designed that trains the residual and attention structures in parallel, thus increasing the classification performance. In the second step, deep features were extracted from the Parallel Attention Residual-Convolutional Neural Network model. The extracted features in the third step were selected by a new and simple algorithm (NCA-ReliefF Matched Index) based on matching the indexes obtained from the Neighborhood Component Analysis and Relief algorithms. In the last process, classification was done with the support vector machine algorithm. The proposed methodology was used for dual and multi-class classification tasks and evaluated on a dataset in the Kaggle database named Severstal: Steel Defect Detection. [ABSTRACT FROM AUTHOR]

The new type of coronavirus disease, which has spread from Wuhan, China since the beginning of 2020 called COVID-19, has caused many deaths and cases in most countries and has reached a global pandemic scale. In addition to test kits, imaging techniques with X-rays used in lung patients have been frequently used in the detection of COVID-19 cases. In the proposed method, a novel approach based on a deep learning model named DeepCovNet was utilized to classify chest X-ray images containing COVID-19, normal (healthy), and pneumonia classes. The convolutional-autoencoder model, which had convolutional layers in encoder and decoder blocks, was trained by using the processed chest X-ray images from scratch for deep feature extraction. The distinctive features were selected with a novel and robust algorithm named SDAR from the deep feature set. In the classification stage, an SVM classifier with various kernel functions was used to evaluate the classification performance of the proposed method. Also, hyperparameters of the SVM classifier were optimized with the Bayesian algorithm for increasing classification accuracy. Specificity, sensitivity, precision, and F-score, were also used as performance metrics in addition to accuracy which was used as the main criterion. The proposed method with an accuracy of 99.75 outperformed the other approaches based on deep learning. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common type of cancer in women. Early diagnosis is very important in this disease. Magnetic Resonance (MR) and Ultrasound (US) are among the most important medical technologies used for early diagnosis. Diagnosis with US is less costly than diagnosis with MR but requires more experience. With the developing technology, automatic decision support systems using artificial intelligence have become extremely popular. At this point, in this study, an automatic diagnosis of breast cancer was tried to be made with a deep learning-based approach using US signals. Since the number of samples used in the study was not large, MobileNetV2, a pre-trained ESA model, was used for feature extraction. In the classification phase, the ensemble RUSBoosted Tree (ERBT) algorithm, which is a community classifier, was preferred. [ABSTRACT FROM AUTHOR]

With the prevalence of obesity in adolescents, and its long-term influence on their overall health, there is a large body of research exploring better ways to reduce the rate of obesity. A traditional way of maintaining an adequate body mass index (BMI), calculated by measuring the weight and height of an individual, is no longer enough, and we are in need of a better health care tool. Therefore, the current research proposes an easier method that offers instant and real-time feedback to the users from the data collected from the motion sensors of a smartphone. The study utilized the mHealth application to identify participants presenting the walking movements of the high BMI group. Using the feedforward deep learning models and convolutional neural network models, the study was able to distinguish the walking movements between nonobese and obese groups, at a rate of 90.5%. The research highlights the potential use of smartphones and suggests the mHealth application as a way to monitor individual health. [ABSTRACT FROM AUTHOR]

The analysis of histopathological images is the core way for detecting breast cancer, the most insidious type of cancer for women. Artificial intelligence-based applications are used as an effective and supportive tool for automated breast cancer detection. Especially, deep learning models are among the most popular approaches due to their high performances in classification problems of medical images. In this study, a novel and robust approach, based on the convolutional-LSTM (CLSTM) learning model, the pre-processing technique using marker-controlled watershed segmentation algorithm (MWSA), and the optimized SVM classifier, was proposed for detecting breast cancer automatically from histopathological images (HPIs). The CLSTM model trained on the BreakHis dataset, which is popular in the research community, composes of binary and eight-class classification tasks. The classification performance of the CLSTM model was significantly increased by using the processed HPIs with MWSA. For binary and eight-class classification tasks, the best scores were obtained by using the optimized SVM classifier with Bayesian optimization instead of the softmax classifier of the CLSTM model. The proposed approach, which provided very high performance for both classification tasks, was compared to the existing approaches using the BreakHis dataset. [ABSTRACT FROM AUTHOR]

[Display omitted] • To work with 2D MR images, it is necessary to choose the slice that best shows the brain tumor. Therefore, 2D deep learning models are not practical. • 3D deep learning models are amenable to practical applications. However, since it contains many slice images without the tumor region, the classification performance is low. • Well-designed customized 3D deep learning-based approaches can perform well in classifying brain tumors. Many machine learning-based studies have been carried out in the literature for the detection of brain tumors using MRI data and most of what has been done in the last 6 years is based on deep learning. Most of them have been designed to work with 2D data. Since many tumor-free slice images are in the models designed in 3D, the classification performance is less than in the 2D models. However, 2D models are unsuitable for practical applications as they use the slice image representing the best tumor image. Therefore, in this study, for brain tumor classification, a new 3 (Attention-Convolutional-LSTM) 3ACL deep learning model that will work with MRI data is presented. Attention, convolutional, and LSTM structures were designed in the same learning architecture in the 3 ACL models, which had an end-to-end learning strategy. Thus, the representation power of the features was increased. In addition, since the proposed model was designed in 3 dimensions, 3D MR images were used directly in the 3ACL model without transforming the 3D MR images into 2D data. Highly representative deep features are extracted from the fully connected layer of the 3ACL model. The feature set is passed to the SVM. Besides, the weighted majority vote technique, which used SVM prediction results conveyed from all slices, improved classification achievement. BRATS 2015 and 2018 datasets were used in this study. For the BRATS 2015 and 2018 datasets, the proposed approach gave 98.90% and 99.29% accuracies, respectively. [ABSTRACT FROM AUTHOR]

The new coronavirus, known as COVID-19, first emerged in Wuhan, China, and since then has been transmitted to the whole world. Around 34 million people have been infected with COVID-19 virus so far, and nearly 1 million have died as a result of the virus. Resource shortages such as test kits and ventilator have arisen in many countries as the number of cases have increased beyond the control. Therefore, it has become very important to develop deep learning-based applications that automatically detect COVID-19 cases using chest X-ray images to assist specialists and radiologists in diagnosis. In this study, we propose a new approach based on deep LSTM model to automatically identify COVID-19 cases from X-ray images. Contrary to the transfer learning and deep feature extraction approaches, the deep LSTM model is an architecture, which is learned from scratch. Besides, the Sobel gradient and marker-controlled watershed segmentation operations are applied to raw images for increasing the performance of proposed model in the pre-processing stage. The experimental studies were performed on a combined public dataset constituted by gathering COVID-19, pneumonia and normal (healthy) chest X-ray images. The dataset was randomly separated into two sections as training and testing data. For training and testing, these separations were performed with the rates of 80%–20%, 70%–30% and 60%–40%, respectively. The best performance was achieved with 80% training and 20% testing rate. Moreover, the success rate was 100% for all performance criteria, which composed of accuracy, sensitivity, specificity and F-score. Consequently, the proposed model with pre-processing images ensured promising results on a small dataset compared to big data. Generally, the proposed model can significantly improve the present radiology based approaches and it can be very useful application for radiologists and specialists to help them in detection, quantity determination and tracing of COVID-19 cases throughout the pandemic. [ABSTRACT FROM AUTHOR]

Recently, there has been an incremental interest on Environmental Sound Classification (ESC), which is an important topic of the non-speech audio classification task. A novel approach, which is based on deep Convolutional Neural Networks (CNN), is proposed in this study. The proposed approach covers a bunch of stages such as pre-processing, deep learning based feature extraction, feature concatenation, feature reduction and classification, respectively. In the first stage, the input sound signals are denoised and are converted into sound images by using the Sort Time Fourier Transform (STFT) method. After sound images are formed, pre-trained CNN models are used for deep feature extraction. In this stage, VGG16, VGG19 and DenseNet201 models are considered. The feature extraction is performed in a pyramidal fashion which makes the dimension of the feature vector quite large. For both dimension reduction and the determination of the most efficient features, a feature selection mechanism is considered after feature concatenation stage. In the last stage of the proposed method, a Support Vector Machines (SVM) classifier is used. The efficiency of the proposed method is calculated on various ESC datasets such as ESC 10, ESC 50 and UrbanSound8K, respectively. The experimental works show that the proposed method produced 94.8%, 81.4% and 78.14% accuracy scores for ESC-10, ESC-50 and UrbanSound8K datasets. The obtained results are also compared with the state-of-the art methods achievements. [ABSTRACT FROM AUTHOR]