Patent Publication Number: US-2021174939-A1

Title: Deep learning system for detecting acute intracranial hemorrhage in non-contrast head ct images

Description:
BACKGROUND 
     This disclosure relates generally to field of medicine, and more particularly to detection of intracranial hemorrhage (ICH). 
     An intracranial hemorrhage (ICH) is a critical condition resulting from bleeding within the skull. ICH accounts for about two million strokes worldwide, and prompt diagnosis is required in order to optimize patient outcomes. Non-contrast computed tomography (CT) scans of a patient&#39;s head are used for initial imaging in cases of head trauma or stroke-like symptoms. 
     SUMMARY 
     Embodiments relate to a method, system, and computer readable medium for detecting intracranial hemorrhage. According to one aspect, a method for detecting intracranial hemorrhage is provided. The method may include receiving, by a computer, data corresponding to a tomograph scan associated with a patient and extracting one or more slices from the received tomograph scan data. One or more adjacent slices may be determined for each of the extracted slices, and the extracted slices and the one or more adjacent slices may be grouped into one or more slabs. The computer may identify one or more features associated with the one or more slab and determine that a slab corresponding to the one or more identified features contains a feature associated with ICH. 
     According to another aspect, a computer system for detecting intracranial hemorrhage is provided. The computer system may include one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, whereby the computer system is capable of performing a method. The method may include receiving, by a computer, data corresponding to a tomograph scan associated with a patient and extracting one or more slices from the received tomograph scan data. One or more adjacent slices may be determined for each of the extracted slices, and the extracted slices and the one or more adjacent slices may be grouped into one or more slabs. The computer may identify one or more features associated with the one or more slab and determine that a slab corresponding to the one or more identified features contains a feature associated with ICH. 
     According to yet another aspect, a computer readable medium for detecting intracranial hemorrhage is provided. The computer readable medium may include one or more computer-readable storage devices and program instructions stored on at least one of the one or more tangible storage devices, the program instructions executable by a processor. The program instructions are executable by a processor for performing a method that may accordingly include receiving, by a computer, data corresponding to a tomograph scan associated with a patient and extracting one or more slices from the received tomograph scan data. One or more adjacent slices may be determined for each of the extracted slices, and the extracted slices and the one or more adjacent slices may be grouped into one or more slabs. The computer may identify one or more features associated with the one or more slab and determine that a slab corresponding to the one or more identified features contains a feature associated with ICH. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other objects, features and advantages will become apparent from the following detailed description of illustrative embodiments, which is to be read in connection with the accompanying drawings. The various features of the drawings are not to scale as the illustrations are for clarity in facilitating one skilled in the art in understanding this disclosure in conjunction with the detailed description. In the drawings: 
         FIG. 1  illustrates a networked computer environment according to at least one embodiment; 
         FIG. 2  is a block diagram of a program that detects intracranial hemorrhage, according to at least one embodiment; 
         FIG. 3  is a functional block diagram of a feature transform filter as depicted in  FIG. 2 , according to at least one embodiment; 
         FIG. 4  is an operational flowchart illustrating the steps carried out by a program that detects intracranial hemorrhage, according to at least one embodiment; 
         FIG. 5  is a block diagram of internal and external components of computers and servers depicted in  FIG. 1  according to at least one embodiment; 
         FIG. 6  is a block diagram of an illustrative cloud computing environment including the computer system depicted in  FIG. 1 , according to at least one embodiment; and 
         FIG. 7  is a block diagram of functional layers of the illustrative cloud computing environment of  FIG. 6 , according to at least one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Detailed embodiments of the claimed structures and methods are disclosed herein; however, it can be understood that the disclosed embodiments are merely illustrative of the claimed structures and methods that may be embodied in various forms. Aspects of this disclosure may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of this disclosure to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments. 
     Embodiments relate generally to the field of medicine, and more particularly to detecting intracranial hemorrhage. The following described exemplary embodiments provide a system, method and program product to, among other things, predict whether adjacent two-dimensional (2D) computed tomography (CT) slices contain a pattern associated with intracranial hemorrhage (ICH). Some embodiments have the capacity to improve the field of medicine by allowing for the use of deep neural networks to augment traditional medical clinical data in diagnosing ICH. Thus, the computer-implemented method, computer system, and computer readable medium disclosed herein may, among other things, be used to determine a correlation between adjacent 2D CT slices, circumvent the intensive computations required for three-dimensional (3D) deep convolutional neural networks (DCNNs), and mitigate the impacts of data imbalance and labelling errors. 
     As previously described, ICH is a critical condition resulting from bleeding within the skull. ICH accounts for about two million strokes worldwide, and prompt diagnosis is required in order to optimize patient outcomes. Non-contrast CT scans of a patient&#39;s head are used for initial imaging in cases of head trauma or stroke-like symptoms. However, CT scans are inherently 3D images. Thus, neural networks may require large amounts of computing power to process and analyze 3D CT scan images. Streamlining the workflow of interpreting a head CT scan by automating the initial triage process may have the potential to substantially decrease the time for diagnosis and may expedite treatment. This may, in turn, decrease morbidity and mortality as a result of stroke and head injury. Automated head CT scan triage systems may be used to automatically manage the priority for interpretation of imaging studies with presumed ICH and help optimize radiology workflow. 
     2D DCNNs may be used to detect ICH in CT images. However, since CT images are inherently 3D, 2D DCNNs may be unable to factor in the correlations between 2D CT slices. Therefore, the performance of head CT triage systems based on 2D DCNN may not yield satisfactory results in clinical practice. To circumvent the limitations of 2D DCNN on detecting ICH in 3D CT images, 3D DCNNs may be used for head CT triage systems. However, although 3D DCNNs may be suitable for analyzing 3D CT images, such 3D DCNNs are computationally intensive to run. For example, due to limited graphics processing unit (GPU) memory, a batch size may, for example, only be set to a size of one for training 3D DCNNs. Additionally, 3D DCNNs may use a much smaller number of training data points than 2D DCNNs. Thus, 3D DCNNs may be limited in their applications in clinical settings. 
     To avoid the restrictions of 2D and 3D DCNNs, it may be advantageous, therefore, to utilize a semi-3D DCNN that may take in a number of 2D head CT slices as inputs and may output the ICH detection on CT images, such that the computation may be comparable to processing 2D images. The correlations between adjacent CT slices may be taken into consideration. Thus, automatic triage of head imaging studies using computer algorithms may have the potential to detect ICH earlier, ultimately leading to improved clinical outcomes. By using a deep learning system to automatically detect acute ICH based on non-contrast head computed tomography (CT) images, a semi-3D deep convolutional neural network (DCNN) may be used to analyze CT images, so that the limitations of 3D DCNN, such as computational intensity, data availability, and the “curse of dimensionality” can be avoided. Moreover, the loss function of the semi-3D DCNN may be modified to address data imbalance and labeling errors in order to circumvent the computational limitation of 3D DCNNs and achieve radiologist-level performance in ICH detection. 
     Aspects are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer readable media according to certain embodiments. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     The following described exemplary embodiments provide a system, method and program product that detects and diagnose intracranial hemorrhage in patients. According to the present embodiment, this detection may be provided through analysis of CT image data through deep learning to detect patterns associated with intracranial hemorrhage. Based on the detection of these patterns, the intracranial hemorrhage may be diagnosed and treated. 
     Referring now to  FIG. 1 , a functional block diagram of a networked computer environment illustrating an intracranial hemorrhage detection system  100  (hereinafter “system”) for improved detection of intracranial hemorrhage is shown. It should be appreciated that  FIG. 1  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements. 
     The system  100  may include a computer  102  and a server computer  114 . The computer  102  may communicate with the server computer  114  via a communication network  110  (hereinafter “network”). The computer  102  may include a processor  104  and a software program  108  that is stored on a data storage device  106  and is enabled to interface with a user and communicate with the server computer  114 . As will be discussed below with reference to  FIG. 5  the computer  102  may include internal components  800 A and external components  900 A, respectively, and the server computer  114  may include internal components  800 B and external components  900 B, respectively. The computer  102  may be, for example, a mobile device, a telephone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing devices capable of running a program, accessing a network, and accessing a database. 
     The server computer  114  may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS), as discussed below with respect to  FIGS. 6 and 7 . The server computer  114  may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud. 
     The server computer  114 , which may be used for detecting, diagnosing, and notifying a user of intracranial hemorrhage is enabled to run an Intracranial Hemorrhage Detection Program  116  (hereinafter “program”) that may interact with a database  112 . The Intracranial Hemorrhage Detection Program method is explained in more detail below with respect to  FIG. 4 . In one embodiment, the computer  102  may operate as an input device including a user interface while the program  116  may run primarily on server computer  114 . In an alternative embodiment, the program  116  may run primarily on one or more computers  102  while the server computer  114  may be used for processing and storage of data used by the program  116 . It should be noted that the program  116  may be a standalone program or may be integrated into a larger intracranial hemorrhage detection program. 
     It should be noted, however, that processing for the program  116  may, in some instances be shared amongst the computers  102  and the server computers  114  in any ratio. In another embodiment, the program  116  may operate on more than one computer, server computer, or some combination of computers and server computers, for example, a plurality of computers  102  communicating across the network  110  with a single server computer  114 . In another embodiment, for example, the program  116  may operate on a plurality of server computers  114  communicating across the network  110  with a plurality of client computers. Alternatively, the program may operate on a network server communicating across the network with a server and a plurality of client computers. 
     The network  110  may include wired connections, wireless connections, fiber optic connections, or some combination thereof. In general, the network  110  can be any combination of connections and protocols that will support communications between the computer  102  and the server computer  114 . The network  110  may include various types of networks, such as, for example, a local area network (LAN), a wide area network (WAN) such as the Internet, a telecommunication network such as the Public Switched Telephone Network (PSTN), a wireless network, a public switched network, a satellite network, a cellular network (e.g., a fifth generation (5G) network, a long-term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a metropolitan area network (MAN), a private network, an ad hoc network, an intranet, a fiber optic-based network, or the like, and/or a combination of these or other types of networks. 
     The number and arrangement of devices and networks shown in  FIG. 1  are provided as an example. In practice, there may be additional devices and/or networks, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than those shown in  FIG. 1 . Furthermore, two or more devices shown in  FIG. 1  may be implemented within a single device, or a single device shown in  FIG. 1  may be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) of system  100  may perform one or more functions described as being performed by another set of devices of system  100 . 
     Referring to  FIG. 2 , a block diagram of an Intracranial Hemorrhage Detection Program  116  is depicted.  FIG. 2  may be described with the aid of the exemplary embodiments depicted in  FIG. 1 . According to one or more embodiments, the Intracranial Hemorrhage Detection Program  116  may be located on the computer  102  ( FIG. 1 ) or on the server computer  114  ( FIG. 1 ). The Intracranial Hemorrhage Detection Program  116  may accordingly include, among other things, a pre-processing module  202  and a deep neural network  204 . The pre-processing module  202  may contain a digital signal processing (DSP) module  208  and may be configured to retrieve data  206 . According to one embodiment, the data  206  may be retrieved from the data storage device  106  ( FIG. 1 ) on the computer  102 . In an alternative embodiment, the data  206  may be retrieved from the database  112  ( FIG. 1 ) on the server computer  114 . The data  206  may include, among other things, CT images collected from a patient. The CT images may have, among other things, different sizes and windows settings. Thus, the preprocessing module  202  may crop the CT images so that only portions of the images corresponding to a patient&#39;s head are analyzed. The preprocessing module  202  may resize the cropped CT images. For example, the CT images may be resized to a size of 256 by 256 pixels. The pixel values of the images may be converted into Hounsfield units and may be filtered to specific values, such as, for example, between 0 and 130. Calcifications present within the images may also be considered when the window settings are chosen. 
     The DSP module  208  may extract one or more 2D CT image slices from the CT data. After preprocessing, adjacent CT slices may be grouped by the DSP module into one or more slabs, in which each slice is used as a channel of the input to a DCNN. It may be appreciated that slabs may be comprised of any number of adjacent CT slices. For example, in the case of a four-slice slab, the slab may considered positive for ICH if patterns associated with ICH are present within the 2 nd  and/or 3 rd  slices. Due to the data imbalance of head CT images, such as the fact that images with ICH features may occur less frequently than normal CT images and that certain types of ICH (e.g. epidural hemorrhages) may occur less frequently than other types of ICH (e.g. intracerebral hemorrhages), oversampling, sample weights and a focal loss function may be used to mitigate the data imbalance effects. Since the head CT slices may be manually labelled, label smoothing and smooth truncated loss function may be employed by the DSP module  208  to address possible labelling errors. Since the CT images may be imbalanced, the focal loss function may be used by the DSP module  208  to alleviate the impact of data imbalance, such that the minority data points may be assigned with larger weights in the loss function. Additionally, label smoothing and smooth truncated loss function may be utilized by the DSP module  208  to mitigate the effects of labelling errors. The DSP module  208  may also apply data cleaning and filtering to the data  206  for better processing by the deep neural network  204 . 
     The deep neural network  204  may include, among other things, an input matrix  210 ; one or more hidden layers  212 ,  214 , and  218 ; a feature transform layer  216 ; a pooling layer  220 ; and one or more connected layers  222  and  224 . It may be appreciated that  FIG. 2  depicts only one implementation of a deep neural network  204 , and that the deep neural network  204  is not limited to these exact layers and order of layers. The deep neural network  204  may contain any number of layers in any order, including adding or omitting any of the depicted layers. 
     The input matrix  210  may, for example, be a two-dimensional matrix with dimensions n by k, whereby n may be a number of CT slices for analysis and k−1 may be a number of adjacent CT slices for each of the CT slices. For example, if 64 CT slices were to be analyzed with three adjacent CT slices for each of the 64 CT slices, the input matrix  210  would have a size of 64 by 4. However, it may be appreciated that n and k may be any values that may be selected based on available computation power, such that more neighborhood information may be kept for each CT slice for larger k values. 
     The feature transform layer  216  may be used to extract one or more features. The feature transform layer  216  is described in further detail with respect to  FIG. 3 . While only one feature transform layer  216  is depicted, it may be appreciated that the deep neural network  204  may contain additional feature transform layers  216  that may be applied to the data  206  in series or in parallel. The one or more hidden layers  212 ,  214 , and  218  may be used to further process the data into a form usable by the deep neural network  204 . The pooling layer  220  may be used to aggregate one or more features and down-sample the data analyzed for ease of identifying one or more features. The pooling layer  220  may apply a max-pooling strategy, an average-pooling strategy, or other pooling methods. The first fully connected layer  222  may be used, for example, to classify the aggregated features and to compare the features to one or more patterns. The patterns may be developed through deep learning, such that no human intervention may be present in the creation of the patterns. The second fully connected layer  224  may be used to classify whether the data  206  contains a pattern associated with intracranial hemorrhage by analyzing the output of the first fully connected layer  222 . The second fully connected layer  224  may, for example, apply an indicator function to the data, such as outputting a “1” if the data contains a pattern associated with intracranial hemorrhage and outputting a “0” if the data does not. The deep neural network  204  may make an identification that ICH patterns are present within the CT data and may transmit this determination to a user, so that the user may, among other things, make any relevant diagnoses. 
     Referring now to  FIG. 3 , a function block diagram of an exemplary feature transform layer  216  is depicted, according to one or more embodiments. Feature transform layer  216  may contain a matrix  302  and a convolutional filter  304 . By way of example and not of limitation, the convolutional filter  304  is depicted as a 2-by-2 matrix having four elements  306 A-D. However, it may be appreciated that the convolutional filter  304  can be substantially any size with any number of elements. The matrix  302  may be, for example, a two-dimensional matrix having dimensions n by k, whereby n represents a number of CT slices for analysis and k−1 represents a number of adjacent slices. Thus, slices  308 A,  310 A, and  312 A through nA may be stored within the first column of matrix  302 . Additionally, adjacent slices  308 B-k,  310 B-k,  312 B-k, and nB-k associated with each of slices  308 A,  310 A,  312 A, and nA, respectively, may be stored in columns two through k of matrix  302 . For example, where slices  308 A,  310 A, and  312 A correspond to adjacent slices, it may be appreciated that slices  308 A,  310 B, and  312 C may be the same, substantially the same, or similar. The convolutional filter  304  may be applied to any or all of the component submatrices (e.g., submatrix A containing slices  308 B,  308 C,  310 B, and  310 C) of the matrix  302  having the same, substantially the same, or similar size as the convolutional filter  304 . The matrix  302 ′ may be generated as a result of calculating the scalar (i.e., dot) product of each of the component submatrices of the matrix  302  and the convolutional filter  304 . For example,  308 B′ may be the dot product of submatrix A and the convolutional filter  304 . 
     Referring now to  FIG. 4 , an operational flowchart  400  illustrating the steps carried out by a program that detects intracranial hemorrhage is depicted.  FIG. 4  may be described with the aid of  FIGS. 1, 2, and 3 . As previously described, the Intracranial Hemorrhage Detection Program  116  ( FIG. 1 ) may quickly and effectively detect intracranial hemorrhage. 
     At  402 , data corresponding to a tomograph scan associated with a patient is received by a computer. The tomograph scan data may include, among other thing, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, a functional magnetic resonance imaging (fMRI) scan, or a positron emission tomography (PET) scan. The tomograph scan data may include images corresponding to a patient&#39;s head. In operation, the Intracranial Hemorrhage Detection Program  116  ( FIG. 1 ) may reside on the computer  102  ( FIG. 1 ) or on the server computer  114  ( FIG. 1 ). The Intracranial Hemorrhage Detection Program  116  may receive data  206  ( FIG. 2 ) over the communication network  110  ( FIG. 1 ) or may retrieve the data  206  from the database  112  ( FIG. 1 ) 
     At  404 , one or more slices from the received tomograph scan data are extracted by the computer. The tomograph scan data may, for example, be received in the form of a 3D tomograph image comprised of one or more 2D image slices. Thus, extracting one or more 2D image slices from the 3D image may allow for a qualitative analysis of the CT data by allowing a comparison between adjacent slices. A number, n, of CT slices may be stored in a column of an n by k two-dimensional matrix. In operation, the DSP module  208  ( FIG. 2 ) may identify one or more 2D CT image slices from among the data  206  ( FIG. 2 ). The DSP module  208  may, for example, store the data  206  in the first column of the input matrix  210  ( FIG. 2 ). 
     At  406 , one or more adjacent slices for each of the extracted slices are determined by the computer. The adjacent CT slices may, among other things provide data for each of the CT slices to be analyzed and may, for example, allow for the detection of unintuitive patterns to assist in diagnosing and treating ICH. The adjacent CT slices may be stored within the second and subsequent columns matrix. There may be, for example, k−1 adjacent CT slices for each of the n CT slices that may be stored in columns  2  through k of the two-dimensional matrix. In operation, the DSP module  208  ( FIG. 2 ) may identify a number of adjacent CT slices for each of the CT slices present within the data  206  ( FIG. 2 ). The DSP module  208  may store this information in the second and subsequent columns of input matrix  210  ( FIG. 2 ) 
     At  408 , the extracted slices and the one or more adjacent slices are grouped into one or more slabs by the computer. Because one or more convolutional filters may be applied to the data, it may be advantageous, for example, to down-sample the data by aggregating features in order to make processing the data more manageable and save on computing resources. In operation, the feature transform layer  216  ( FIG. 2 ) may apply a convolutional filter  304  ( FIG. 3 ) to the matrix  302  ( FIG. 3 ). The convolutional filter  304  may be, for example, a size 2-by-2 array and may be applied to matrix  302  by calculating a dot product for each of the component 2-by-2 arrays of the matrix  302 . Thus, a matrix  302 ′ ( FIG. 3 ) having a size (k−1)-by-(n−1) may be produced as a result of applying the convolutional filter  304  to the matrix  302 . It may be appreciated that one or more convolutional filters  304  may be applied to the matrix  302  simultaneously, yielding one or more matrices  302 ′. These matrices  302 ′ may be appended to one another by, for example, the hidden layer  218  ( FIG. 2 ) to create a higher-order multi-dimensional array. The pooling layer  220  ( FIG. 2 ) may apply one or more pooling strategies to the matrix  302 ′, such as max-pooling or average-pooling. For example, the pooling layer  220  may apply max-pooling to the matrix  302 ′ such that the maximum value present in each non-overlapping 2-by-2 component submatrix of the matrix  302 ′ may be placed into a cell in a matrix having an approximate size (n−1)/2-by-(k−1)/2. 
     At  410 , one or more features associated with the one or more slabs are identified by the computer. After the features have been aggregated, the system may identify one or more patterns from among the features, such as patterns associated with ICH. In operation, the first fully connected layer  222  ( FIG. 2 ) of the deep neural network  204  ( FIG. 2 ) may analyze the down-sampled matrix output by the pooling layer  220  ( FIG. 2 ) to determine if any patterns consistent with ICH are present within the data  206  ( FIG. 2 ). If any patterns are detected, the system may accordingly classify them based on the presence of such patterns. 
     At  412 , the computer determines that a slab corresponding to the one or more identified features contains a feature associated with ICH. After determining the presence of one or more patterns present within the data, the computer may, among other things, determine whether one or more of these patterns correspond to ICH. By learning, through patterns in the data, whether the data contains ICH, identification of such a condition can be made without human intervention and without bias in the development of the model. In operation, the second fully connected layer  224  ( FIG. 2 ) of the deep neural network  204  ( FIG. 2 ) may apply a filter to the output of the first fully connected layer  222  ( FIG. 2 ) to determine whether there exists a pattern in the data  206  that corresponds to ICH. The second fully connected layer  224  may output, for example, a “1” if it determines that an ICH pattern may be present with the data  206 . The second fully connected layer  224  may additionally output, for example, a “0” if it determines that an ICH pattern may not be present within the data  206 . 
     It may be appreciated that  FIG. 4  provides only an illustration of one implementation and does not imply any limitations with regard to how different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements. 
       FIG. 5  is a block diagram  500  of internal and external components of computers depicted in  FIG. 1  in accordance with an illustrative embodiment. It should be appreciated that  FIG. 5  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements. 
     Computer  102  ( FIG. 1 ) and server computer  114  ( FIG. 1 ) may include respective sets of internal components  800 A,B and external components  900 A,B illustrated in  FIG. 5 . Each of the sets of internal components  800  include one or more processors  820 , one or more computer-readable RAMs  822  and one or more computer-readable ROMs  824  on one or more buses  826 , one or more operating systems  828 , and one or more computer-readable tangible storage devices  830 . 
     Processor  820  is implemented in hardware, firmware, or a combination of hardware and software. Processor  820  is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another type of processing component. In some implementations, processor  820  includes one or more processors capable of being programmed to perform a function. Bus  826  includes a component that permits communication among the internal components  800 A,B. 
     The one or more operating systems  828 , the software program  108  ( FIG. 1 ) and the Intracranial Hemorrhage Detection Program  116  ( FIG. 1 ) on server computer  114  ( FIG. 1 ) are stored on one or more of the respective computer-readable tangible storage devices  830  for execution by one or more of the respective processors  820  via one or more of the respective RAMs  822  (which typically include cache memory). In the embodiment illustrated in  FIG. 5 , each of the computer-readable tangible storage devices  830  is a magnetic disk storage device of an internal hard drive. Alternatively, each of the computer-readable tangible storage devices  830  is a semiconductor storage device such as ROM  824 , EPROM, flash memory, an optical disk, a magneto-optic disk, a solid state disk, a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and/or another type of non-transitory computer-readable tangible storage device that can store a computer program and digital information. 
     Each set of internal components  800 A,B also includes a R/W drive or interface  832  to read from and write to one or more portable computer-readable tangible storage devices  936  such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. A software program, such as the software program  108  ( FIG. 1 ) and the Intracranial Hemorrhage Detection Program  116  ( FIG. 1 ) can be stored on one or more of the respective portable computer-readable tangible storage devices  936 , read via the respective R/W drive or interface  832  and loaded into the respective hard drive  830 . 
     Each set of internal components  800 A,B also includes network adapters or interfaces  836  such as a TCP/IP adapter cards; wireless Wi-Fi interface cards; or 3G, 4G, or 5G wireless interface cards or other wired or wireless communication links. The software program  108  (FIG.  1 ) and the Intracranial Hemorrhage Detection Program  116  ( FIG. 1 ) on the server computer  114  ( FIG. 1 ) can be downloaded to the computer  102  ( FIG. 1 ) and server computer  114  from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces  836 . From the network adapters or interfaces  836 , the software program  108  and the Intracranial Hemorrhage Detection Program  116  on the server computer  114  are loaded into the respective hard drive  830 . The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. 
     Each of the sets of external components  900 A,B can include a computer display monitor  920 , a keyboard  930 , and a computer mouse  934 . External components  900 A,B can also include touch screens, virtual keyboards, touch pads, pointing devices, and other human interface devices. Each of the sets of internal components  800 A,B also includes device drivers  840  to interface to computer display monitor  920 , keyboard  930  and computer mouse  934 . The device drivers  840 , R/W drive or interface  832  and network adapter or interface  836  comprise hardware and software (stored in storage device  830  and/or ROM  824 ). 
     It is understood in advance that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, some embodiments are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes. 
     Referring to  FIG. 6 , illustrative cloud computing environment  600  is depicted. As shown, cloud computing environment  600  comprises one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Cloud computing nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  600  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG. 6  are intended to be illustrative only and that cloud computing nodes  10  and cloud computing environment  600  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring to  FIG. 7 , a set of functional abstraction layers  700  provided by cloud computing environment  600  ( FIG. 6 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 7  are intended to be illustrative only and embodiments of the disclosure are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and Intracranial Hemorrhage Detection  96 . Intracranial Hemorrhage Detection  96  may detect and classify patterns associated with intracranial hemorrhage in a patient. 
     Some embodiments may relate to a system, a method, and/or a computer readable medium at any possible technical detail level of integration. The computer readable medium may include a computer-readable non-transitory storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out operations. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program code/instructions for carrying out operations may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects or operations. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer readable media according to various embodiments. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). The method, computer system, and computer readable medium may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in the Figures. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed concurrently or substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     It will be apparent that systems and/or methods, described herein, may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and/or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and/or methods were described herein without reference to specific software code—it being understood that software and hardware may be designed to implement the systems and/or methods based on the description herein. 
     No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items, and may be used interchangeably with “one or more.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, etc.), and may be used interchangeably with “one or more.” Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. 
     The descriptions of the various aspects and embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Even though combinations of features are recited in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and/or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.