Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs.

Covid-19 is a disease that affects the upper and lower respiratory tract and has fatal consequences in individuals. Early diagnosis of COVID-19 disease is important. Datasets used in this study were collected from hospitals in Istanbul. The first dataset consists of COVID-19, viral pneumonia, and bacterial pneumonia types. The second dataset consists of the following findings of COVID-19: ground glass opacity, ground glass opacity, and nodule, crazy paving pattern, consolidation, consolidation, and ground glass. The approach suggested in this paper is based on artificial intelligence. The proposed approach consists of three steps. As a first step, preprocessing was applied and, in this step, the Fourier Transform and Gradient-weighted Class Activation Mapping methods were applied to the input images together. In the second step, type-based activation sets were created with three different ResNet models before the Softmax method. In the third step, the best type-based activations were selected among the CNN models using the local interpretable model-agnostic explanations method and re-classified with the Softmax method. An overall accuracy success of 99.15% was achieved with the proposed approach in the dataset containing three types of image sets. In the dataset consisting of COVID-19 findings, an overall accuracy success of 99.62% was achieved with the recommended approach.

Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks.

Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learning model called BrainMRNet which is developed for mass detection in open-source brain magnetic resonance images was used. The BrainMRNet model includes three processing steps: attention modules, the hypercolumn technique, and residual blocks. To demonstrate the accuracy of the proposed model, three types of tumor data leading to brain cancer were examined in this study: glioma, meningioma, and pituitary. In addition, a segmentation method was proposed, which additionally determines in which lobe area of the brain the two classes of tumors that cause brain cancer are more concentrated. The classification accuracy rates were performed in the study; it was 98.18% in glioma tumor, 96.73% in meningioma tumor, and 98.18% in pituitary tumor. At the end of the experiment, using the subset of glioma and meningioma tumor images, it was determined which at brain lobe the tumor region was seen, and 100% success was achieved in the analysis of this determination. In this study, a hybrid deep learning model is presented to determine the detection of the brain tumor. In addition, open-source software was proposed, which statistically found in which lobe region of the human brain the brain tumor occurred. The methods applied and tested in the experiments have shown promising results with a high level of accuracy, precision, and specificity. These results demonstrate the availability of the proposed approach in clinical settings to support the medical decision regarding brain tumor detection.

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches.

Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.

Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders.

Invasive ductal carcinoma cancer, which invades the breast tissues by destroying the milk channels, is the most common type of breast cancer in women. Approximately, 80% of breast cancer patients have invasive ductal carcinoma and roughly 66.6% of these patients are older than 55 years. This situation points out a powerful relationship between the type of breast cancer and progressed woman age. In this study, the classification of invasive ductal carcinoma breast cancer is performed by using deep learning models, which is the sub-branch of artificial intelligence. In this scope, convolutional neural network models and the autoencoder network model are combined. In the experiment, the dataset was reconstructed by processing with the autoencoder model. The discriminative features obtained from convolutional neural network models were utilized. As a result, the most efficient features were determined by using the ridge regression method, and classification was performed using linear discriminant analysis. The best success rate of classification was achieved as 98.59%. Consequently, the proposed approach can be admitted as a successful model in the classification.

BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model.

A brain tumor is a mass that grows unevenly in the brain and directly affects human life. This mass occurs spontaneously because of the tissues surrounding the brain or the skull. Surgical methods are generally preferred for the treatment of the brain tumor. Recently, models of deep learning in the diagnosis and treatment of diseases in the biomedical field have gained intense interest. In this study, we propose a new convolutional neural network model named BrainMRNet. This architecture is built on attention modules and hypercolumn technique; it has a residual network. Firstly, image is preprocessed in BrainMRNet. Then, this step is transferred to attention modules using image augmentation techniques for each image. Attention modules select important areas of the image and the image is transferred to convolutional layers. One of the most important techniques that the BrainMRNet model uses in the convolutional layers is hypercolumn. With the help of this technique, the features extracted from each layer of the BrainMRNet model are retained by the array structure in the last layer. The aim is to select the best and the most efficient features among the features maintained in the array. Accessible magnetic resonance images were used to detect brain tumor with the BrainMRNet model. BrainMRNet model is more successful than the pre-trained convolutional neural network models (AlexNet, GoogleNet, VGG-16) used in this study. The classification success achieved with the BrainMRNet model was 96.05%.

Texture based feature extraction methods for content based medical image retrieval systems.

The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extraction for medical image retrieval systems. The investigated algorithms in this study depend on gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and Gabor wavelet accepted as spatial methods. In the experiments, the database is built including hundreds of medical images such as brain, lung, sinus, and bone. The results obtained in this study shows that queries based on statistics obtained from GLCM are satisfied. However, it is observed that Gabor Wavelet has been the most effective and accurate method.

Evaluation of internal auditory canal structures in tinnitus of unknown origin.

OBJECTIVES: The aim of the present study was to evaluate the internal auditory canal (IAC) and the nerves inside it to define possible structural differences in cases with subjective tinnitus of unknown origin. METHODS: Cases applying to the ear, nose and throat department with the complaint of tinnitus with unknown origin and having normal physical examination and test results were included in the study (n=78). Patients admitted to the radiology clinic for routine cranial magnetic resonance imaging (MRI) and whose MRI findings revealed no pathologies were enrolled as the control group (n=79). Data for the control group were obtained from the radiology department and informed consent was obtained from all the patients. Diameters of the IAC and the nerves inside it were measured through enhanced images obtained by routine temporal bone MRIs in all cases. Statistical evaluations were performed using Student t-test and statistical significance was defined as P<0.05. RESULTS: Measurements of IAC diameters revealed statistically significant differences between the controls and the tinnitus group (P<0.05). Regarding the diameters of the cochlear nerve, facial nerve, inferior vestibular nerve, superior vestibular nerve, and total vestibular nerve, no statistically significant difference was found between the controls and the tinnitus group. CONCLUSION: Narrowed IAC has to be assessed as an etiological factor in cases with subjective tinnitus of unknown origin.

A fusion method of Gabor wavelet transform and unsupervised clustering algorithms for tissue edge detection.

This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.

Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study.

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.