Background: The need for accurate and timely detection of Intracranial hemorrhage (ICH) is utmost important to avoid untoward incidents that may even lead to death.Hence, this presented work leverages the ability of a pretrained deep convolutional neural network (CNN) for the detection of ICH in computed tomography (CT) brain images. Methods: Different frameworks have been analyzed for their effectiveness for the classification of CT brain images into hemorrhage or non-hemorrhage conditions. All these frameworks were investigated on CQ500 dataset. Furthermore, an exclusive preprocessing pipeline was designed for both normal and ICH CT images. Firstly, a framework involving the pretrained deep CNN, AlexNet, has been exploited for both feature extraction and classification using the transfer learning method, secondly, a modified AlexNet-Support vector machine (SVM) classifier is explored and finally, a feature selection method, Principal Component Analysis (PCA) has been introduced in the AlexNet-SVM classifier model and its efficacy is explored.These models were trained and tested on two different sets of CT images, one containing the original images without preprocessing and another set consisting of preprocessed images. Results: The modified AlexNet-SVM classifier has shown an improved performance in comparison to the other investigated frameworks and has achieved a classification accuracy of 99.86%, sensitivity and specificity of 0.9986 for the detection of ICH in brain CT images. Conclusion: This research has given an overview of a simple and efficient framework for the classification of hemorrhage and non-hemorrhage images. Also, the proposed simplified deep learning framework manifests its ability as a screening tool to assist the radiological trainees for the accurate detection of ICH.
Adolescent Idiopathic Scoliosis (AIS) is a musculoskeletal condition commonly seen in pediatric children that causes deformity of the spine. The study aims for early detection and diagnosis as these are the possible options to delimit the progression of the disorder. The work has explored the development of an algorithm that could detect the landmarks and extract the shape-based features from the markerless 3D surface data in AIS patients. An approach to classifying these extracted features using the machine learning algorithm, Support Vector Machine (SVM), has been investigated. The objectives of the work were divided into three frameworks. Framework-1 is aimed at classifying the data based on the asymmetry pattern observed in the spinal surface of the patients. The data corresponding to normal posture were considered as 'without deformity' and data with an asymmetry spinal curve were considered as 'with deformity' based on indicators extracted using the ScolioSIM tool. Framework-2 is aimed at classifying the AIS patients' data based on the three deformity levels namely, mild, moderate and severe. Framework-3 is aimed at classifying the shape orientation of the AIS condition as right or left based on the extracted shape features. The SVM algorithm was able to classify the asymmetry spinal surface pattern and the three deformity levels with accuracy values of 72.4% and 80%, respectively. Furthermore, an accuracy of 94.9% was obtained to classify the shape orientation either as right-or left-oriented. Hence, this noninvasive diagnosis and assessment study paves a new way of approach for the 2D and 3D shape classifications of AIS and expedites the treatment planning process.
ObjectivesThis work describes the design and development of a four-channel near-infrared spectroscopy system to detect the oxygenated and deoxygenated hemoglobin concentration changes in the brain during various motor tasks.MethodsThe system uses light-emitting diodes corresponding to two wavelengths of 760 nm and 850 nm sensitive to deoxygenated and oxygenated hemoglobin concentration changes, respectively. The response is detected using a photodetector with an integrated transimpedance amplifier. The system is designed with four channels for functional near-infrared spectroscopy (fNIRS) signals acquisition. Two experiments were conducted to demonstrate the ability of the system to detect the changes in hemodynamic responses of different tasks. In the first experiment, the hemodynamic changes during motor execution and imagery of right- and left-fist clenching tasks were acquired by the developed system and validated against a standard multichannel NIRS system. In another experiment, the fNIRS signals during rest and motor execution of right-fist clenching task were acquired using the system and classified.ResultsThe results demonstrate the ability of the designed system to detect the brain hemodynamic changes during various tasks. Also, the activation patterns obtained by the developed system with a minimum number of channels are on par with those obtained by the commercial system.ConclusionsThe developed four-channel NIRS system is user-friendly and has been designed with inexpensive components, unlike the commercially available NIRS instruments that are cumbersome and expensive.
Neuromarketing merges viewpoints of marketing, neuroscience, economics, choice hypothesis that are required to analyze the psychology of consumers’ preference to product development. The traditional methods involve product ratings, conducting questionnaire surveys that stumble upon verbal declarations provided by the vendees. Consumer Neuroscience describes the emotional, cognitive aspects that form the base of human decision making. Our study aims to utilize the neuroscientific information that distinguishes contrasts between healthy subjects’ EEG signals for examining the brain activity during visual and gustatory stimuli of different flavours of a beverage brand. The EEG montage assigned according to brain-region-specific localization draws out the subjects’ true elicited subconscious response regardless of whether the subject attempts to control his/her affective state. The results showed the activation of theta and delta bands of EEG signals during the given stimuli. These elicited signal variations can be used to identify the best favoured item for successful product dispatch and reduction in loss. Another major application is directed towards the customization of liquid food intake of locked-in, comatose, vegetative state patients by observing their brain response to the various fluid intake and determining the best response among them. This aids physicians to put the patients on a path to recovery.
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