The present invention relates to the field of the system and method for delayed enhancement magnetic resonance imaging and artificial intelligence to identify non-viable myocardial tissue and identify COVID 19. The invention more particularly relates to a system based on deep learning for analyzing delayed enhancement magnetic resonance imaging to identify COVID 19 and method thereof.
BACKGROUND OF THE INVENTION
[002] Now-a-days, blockchain technology is becoming popular because of having the characteristics of decentralization, openness and transparency, each computing device can participate in database records, and the rapid data synchronization between computing devices, the blockchain technology has been widely used in many fields to apply.
[003] The novel Coronavirus, COVID-19, pandemic is being considered the most critical health calamity of the century. Many research organizations have come together during this crisis and created various Deep Learning data models for the effective diagnosis of COVID-19 from chest radiography or MRI images. Such as, The University of Waterloo, along with Darwin AI—a start-up spin-off of this department, has designed the Deep Learning model ‘COVID-Net’ and created a dataset called ‘COVIDx’ consisting of 13,975 images across 13,870 patient cases. In this study, COGNEX’s Deep Learning Software, VisionPro Deep Learnin, is used to classify these Chest X-rays from the COVIDx dataset. The results are compared with the results of COVID-Net and various other state-of-the-art Deep Learning models from the open-source community. Deep Learning tools are often referred to as black boxes because humans cannot interpret how or why a model is classifying an image into a particular class. This problem is addressed by testing VisionPro Deep Learning with two settings, first, by selecting the entire image as the Region of Interest (ROI), and second, by segmenting the lungs in the first step, and then doing the classification step on the segmented lungs only, instead of using the entire image. VisionPro Deep Learning results: on the entire image as the ROI it achieves an overall F score of 94.0%, and on the segmented lungs, it gets an F score of 95.3%, which is better than COVID-Net and other state-of-the-art open-source Deep Learning models.
[004] Hence, the dataset consists of COVID-19 X-ray, CT or MRI scan images. It turns out that the most frequently used view is the Posteroanterior view and I have considered the COVID-19 PA view X-ray scans for my analysis. To stratify the final data the present invention will take an equal number of images and will blend them and later will divide into test and train data.
[005] Since, the present invention have already utilized the data which is the most tedious part of this invention, let’s move to the next step, in which it will create a deep learning model that is going to learn the difference between normal X-Ray and COVID-19 affected X-Ray.
[006] Accordingly, there is therefore a need for a system based on deep learning for analyzing delayed enhancement magnetic resonance imaging to identify COVID 19 and method thereof. It would be preferable if the system and method with easy conventional equipment implementation and is more compact and gives accurate result than the conventional systems and methods currently in use for diagnosing COVID 19. Therefore, it would be useful and desirable to have a system, method, apparatus and interface to meet the above-mentioned needs.
SUMMARY OF THE PRESENT INVENTION
[007] In view of the foregoing disadvantages inherent in the known types of conventional COVID 19 identification system, method and devices, are now present in the prior art, the present invention provides a system based on deep learning for analyzing delayed enhancement magnetic resonance imaging to identify COVID 19 and method thereof. The system is designed with, but not limited to, at least two set equipment implementation phase, in which the first set of equipment is the physical placement of the computation servers in the computer network, which is communicatively coupled with the second set of equipment implemented with the help of a processing unit provided for data modelling by using deep learning and based on a machine learning and an Artificial Intelligence trained embedded software and algorithm, which has all the advantages of the prior art and none of the disadvantages.
[008] The main aspect of the present invention is to provide a system, which comprises a processing unit provided with hyperparameters to produce 96.4% accuracy, which isn’t bad but still can be improved as, if the present invention is deployed with a model with around 96.4% accuracy in real-time scenarios, wherein the wrongly identified patients still can spread the disease and the goal of the present invention for an efficient approach that must be able to achieve success.
[009] Another aspect of the present invention is to provide a system, in which the processing unit on receiving an image data is configured to utilize contrast to strengthen helps discern non-viable regions for COVID 19 identification. The process of image processing is done by different signal intensity indication activated information with single photon emission tomography (SPECT). Further, the purpose of the present invention is not limited with only resolution, which is quite low, and unavailable usually under some situation, but further data modelling of the data through deep learning.
[010] The proposed system and method is implemented on, but not limited to, the Field Programmable Gate Arrays (FPGAs) and the like, PC, Microcontroller and with other known processors to have computer algorithms and instruction up gradation for supporting many applications domain where the aforesaid problems to solution is required.
[011] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[012] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS
[013] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
[014] FIG. 1 illustrates a schematic diagram of a system based on deep learning for analyzing delayed enhancement magnetic resonance imaging to identify COVID 19 and method thereof, in accordance with an embodiment of the present invention; and
[015] FIG. 2 illustrates a block diagram of the system based on deep learning for analyzing delayed enhancement magnetic resonance imaging to identify COVID 19 and method thereof, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[016] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one” and the word “plurality” means “one or more” unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[017] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase “comprising”, it is understood that we also contemplate the same composition, element or group of elements with transitional phrases “consisting of”, “consisting”, “selected from the group of consisting of, “including”, or “is” preceding the recitation of the composition, element or group of elements and vice versa.
[018] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[019] Referring now to the drawings, these are illustrated in FIG. 1, the present invention discloses a system based on deep learning for analyzing delayed enhancement magnetic resonance imaging to identify COVID 19 and method thereof. The method and system are comprised of, but not limited to, a processing unit adapted to process the data based on deep learning data modelling in the magnetic resonance imaging associated with the digital image scanning system for diagnosis COVID 19 with the spatial resolution that each frame is deposited is 256 * 256, and being creating that level and vertical resolution respectively are 256 pixels (pixel), the read/write address that the read/write address of each image element, which is controlled by processing unit and forms circuit and finishes.
[020] In accordance with another embodiment of the present invention, the data that will be stored in the memory unit are input to a real-time microcontroller, it is characterized in that: analyze and compare by the Multi-source Information Fusion analytical system by using the real-time microcontroller to deliver the D/A changer then, digital signal is become analogue signal output.
[021] In accordance with another embodiment of the present invention, the processing unit is configured to process an image segmentation of the multiple medical images, describing image segmentation includes, but not limited to, an isolating sense from each image and the region of interest, and further, the image application cascade deep convolutional neural networks split is detected.
[022] In accordance with another embodiment of the present invention, the processing unit is configured to create multiple levels of the convolutional neural network, which is used for by using random yardstick and arbitrary viewing angles in each 3D At least one random site to diagnose COVID 19, which is selected to screen the multiple described 3D image bodies constructed by the position candidate in vivo.
[023] In accordance with another embodiment of the present invention, the multiple medical images are configured to receive through, but not limited to, from CT scan, PET/CT scanning, MRI, ultrasound, X-ray, SPECT sweeping Retouch, angiogram, fluorescence photo, mammogram, microphoto or its combination.
[024] In accordance with another embodiment of the present invention, the processing unit is configured to provide a neural network through a user interface, which is included from multiple neural networks randomly selected at least one neural network example to diagnose the COVID 19, and further, included from multiple neural networks randomly selected one or more neural networks in diagnose of COVID 19.
[025] In accordance with another embodiment of the present invention, the processing unit is further configured to select the image having myocardium part segmentation with a patient, be categorised into endocardial border and epicardial edge, and described processing unit also divided into covering of the image with the myocardial wall of described cardiac muscle part.
[027] Further, various exemplary computer system for implementing embodiments consistent with the present disclosure. Variations of computer system may be used for implementing the system based on deep learning for analyzing delayed enhancement magnetic resonance imaging to identify COVID 19 and method thereof. Computer system may comprise a central processing unit (“CPU” or “processor”). Processor may comprise at least one data processor for executing program components for executing user or system-generated requests. A user may include a person, a person using a device such as such as those included in this disclosure, or such a device itself. The processor may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. The processor may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM’s application, embedded or secure processors, IBM PowerPC, Intel’s Core, Itanium, Xeon, Celeron or other line of processors, etc. The processor may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), Field Programmable Gate Arrays (FPGAs), etc.
[028] Processor may be disposed in communication with one or more input/output (I/O) devices via I/O interfaces. The I/O interfaces may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.
[029] In some embodiments, the processor may be disposed in communication with one or more memory devices (e.g., RAM, ROM, etc.) via a storage interface. The storage interface may connect to memory devices including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), integrated drive electronics (IDE), IEEE-1394, universal serial bus (USB), fiber channel, small computer systems interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, redundant array of independent discs (RAID), solid-state memory devices, solid-state drives, etc. The memory devices may store a collection of program or database components, including, without limitation, an operating system, user interface application, web browser, mail server, mail client, user/application data(e.g., any data variables or data records discussed in this disclosure), etc. The operating system may facilitate resource management and operation of the computer system. Examples of operating systems include, without limitation, Apple Macintosh OS X, Unix, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), IBM OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry OS, or the like.
[030] The word “module,” “model” “algorithms” and the like as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a programming language, such as, for example, Java, C, Python or assembly. One or more software instructions in the modules may be embedded in firmware, such as an EPROM. It will be appreciated that modules may comprised connected logic units, such as gates and flip-flops, and may comprise programmable units, such as programmable gate arrays or processors. The modules described herein may be implemented as either software and/or hardware modules and may be stored in any type of computer-readable medium or other computer storage device. Further, in various embodiments, the processor is one of, but not limited to, a general-purpose processor, an application specific integrated circuit (ASIC) and a field-programmable gate array (FPGA) processor. Furthermore, the data repository may be a cloud-based storage or a hard disk drive (HDD), Solid state drive (SSD), flash drive, ROM or any other data storage means.
[032] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[033] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
[034] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention.

We Claim:

1. A system based on deep learning for analyzing delayed enhancement magnetic resonance imaging to identify COVID 19, comprising:
a processing unit adapted to process the data based on deep learning data modelling in the magnetic resonance imaging associated with the digital image scanning system for diagnosis COVID 19 with the spatial resolution that each frame is deposited is 256 * 256, and being creating that level and vertical resolution respectively are 256 pixels (pixel), the read/write address that the read/write address of each image element, which is controlled by processing unit and forms circuit and finishes; And the data that will be stored in memory are input to a real-time microcontroller, it is characterized in that: analyze and compare by the Multi-source Information Fusion analytical system by using the real-time microcontroller to deliver the D/A changer then, digital signal is become analogue signal output.
2. The system as claimed in claim 1, wherein the processing unit is configured to process an image segmentation of the multiple medical images, describing image segmentation includes, but not limited to, an isolating sense from each image and the region of interest, and further, the image application cascade deep convolutional neural networks split is detected.
3. The system as claimed in claim 1, wherein the processing unit is configured to create multiple levels of the convolutional neural network, which is used for by using random yardstick and arbitrary viewing angles in each 3D At least one random site to diagnose COVID 19, which is selected to screen the multiple described 3D image bodies constructed by the position candidate in vivo.
4. The system as claimed in claim 1, wherein the multiple medical images are configured to receive through, but not limited to, from CT scan, PET/CT scanning, MRI, ultrasound, X-ray, SPECT sweeping Retouch, angiogram, fluorescence photo, mammogram, microphoto or its combination.
5. The system as claimed in claim 1, wherein the processing unit is configured to provide a neural network through a user interface, which is included from multiple neural networks randomly selected at least one neural network example to diagnose the COVID 19, and further, included from multiple neural networks randomly selected one or more neural network in diagnose of COVID 19.
6. The system as claimed in claim 1, wherein the processing unit is further configured to select the image having myocardium part segmentation with a patient, be categorized into endocardial border and epicardial edge, and described processing unit also divided into covering of the image with the myocardial wall of described cardiac muscle part.
7. The system as claimed in claim 1, wherein wherein the processing unit with the memory unit is further resided in a computation server and communicatively connected to the end-terminals and provided with compatible interfaces and a set of data processing operation synchronization devices connected to a computer network.
8. A method based on deep learning for analyzing delayed enhancement magnetic resonance imaging to identify COVID 19, comprising the steps of:
Providing, a processing unit adapted to process the data based on deep learning data modelling in the magnetic resonance imaging associated with the digital image scanning system for diagnosis COVID 19 with the spatial resolution that each frame is deposited is 256 * 256, and being creating that level and vertical resolution respectively are 256 pixels (pixel), the read/write address that the read/write address of each image element, which is controlled by processing unit and forms circuit and finishes; And the data that will be stored in a memory unit are input to a real-time microcontroller, it is characterized in that: analyze and compare by the Multi-source Information Fusion analytical system by using the real-time microcontroller to deliver the D/A changer then, digital signal is become analogue signal output.