The present invention relates to the field of systems and methods for optical detection of skin disease and in particular apparatus and methods adapted to detect the presence of melanoma and to distinguish, for example, but not limited to, malignant melanoma from non-malignant dysplastic nevi and/or common nevi, using metrics and classifiers obtained from rotational analysis of image data obtained from a subject's skin lesion. The invention more particularly relates to a system for skin cancer disease detection using machine learning modules and method thereof.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Melanoma, the most lethal skin cancer, incurs immense human and financial cost. Early detection is critical to prevent metastasis by removal of primary tumors. The early lateral growth phase is a vastly preferable detection window to the subsequent phase of metastatic initiation. Optical detection technologies for automated quantitative metrics of malignancy are needed to more accurately guide decisions regarding the need to biopsy and to make preoperative determination of adequate margins for surgical excision.
[004] Another issue comes here that after invasive biopsy or excision, diagnosis obtained by histopathologic evaluation is nearly 100% accurate;
nevertheless, deciding which lesions to biopsy is challenging. Only few percentage of surgically-excised pigmented lesions are diagnosed as melanomas. Hence there is a need for non-invasive screening mechanisms that are both widespread and more accurate by using ML & AI based interfaces.
[005] Accordingly, on the basis of aforesaid facts, there remains a need in the prior art to provide an image processing system for skin cancer disease detection using machine learning modules and method thereof. The proposed system overcomes the problem to use of conventional and complex techniques and helps to provide a system for image processing having, an an optical coherence device, storage medium & electronic device and method thereof. 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
[006] In view of the foregoing disadvantages inherent in the known types of conventional skin disease detection system and techniques, are now present in the prior art, the present invention provides an image processing system for skin cancer disease detection using machine learning modules and method thereof, which has all the advantages of the prior art and none of the disadvantages. The object of the present invention is to avoid the above-mentioned problems and create a unified system for providing the better and efficient image processing schemes and techniques.
[007] The main aspect of the present invention is to provide a system, in which an interactive display device to receive multiple concurrent user inputs or lesion images in order to execute intuitive or user-configured operations on a portable computing system is disclosed. Further, the portable computing system with a touchscreen device includes, but not limited to, an interactive display device for performing an operation on the portable computing system in response to user input or gestures is disclosed.
[008] Another aspect of the present invention, in which the system is provided with a method of perform evaluations of image data obtained from reflecting light off of multiple skin lesions with greater sensitivity, specificity and overall diagnostic accuracy, and which can be used to produce diagnostically relevant quantitative metrics in real time by using ML & AI based interfaces and methods, in some cases without further evaluation of the lesion.
[009] The proposed system and method is implemented on the processing unit functioning with, 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.
[010] 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.
[011] 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
[012] 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:
[013] FIG. 1, illustrates a schematic diagram of a system for skin cancer disease detection using machine learning modules and method thereof, in accordance with an embodiment of the present invention; and
[014] FIG. 2, illustrates another block diagram of the system for skin cancer disease detection using machine learning modules and method thereof, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[015] 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.
[016] 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.
[017] 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.
[018] Referring now to the drawings, these are illustrated in FIG. 1 & 2, the present invention discloses a system for skin cancer disease detection using machine learning modules and method thereof. The system is comprised of, but not limited to, an image processing device having a microscopic lens, a light source for generating incident light and a transmission unit for transmitting a microscopy image to an image processing unit.
[019] In accordance with another embodiment of the present invention, an optical coherence device having a machine learning based user interface, comprising a light processing unit for emitting and capturing coherence light and a processing unit having a microcontroller unit for controlling the microscope and the optical coherence device.
[020] In accordance with another embodiment of the present invention, the processing unit having the microcontroller unit controls the microscope and the optical coherence device such that the optical coherence device is activated only in consequence of a preceding operation of the microscope and upon fulfilment of an activation criterion for skin cancer disease detection.
[021] In accordance with another embodiment of the present invention, the image processing device is provided with a mechanical fixture having a flat surface to position or press against the subject's skin to define a distal imaging plane containing a lesion and adapted to obtain image data from light reflected by the distal imaging plane.
[022] In accordance with another embodiment of the present invention, the processing unit having the microcontroller unit adapted to process the image data with a machine learning based algorithm to obtain metrics and/or one or more classifiers defining the rotational symmetry of the lesion for skin cancer disease detection.
[023] In accordance with another embodiment of the present invention, the machine learning based algorithm comprises evaluating pixels on a line segment between the center of the lesion image and the border of the lesion image as the line segment rotates about the center of the lesion image for skin cancer disease detection.
[024] In accordance with another embodiment of the present invention, the machine learning based algorithm comprises estimating values which are statistical measures of local intensity variation in the digital images in each spectral band, which are a function of the texture of the region of interest. Further, the processing unit having the microcontroller unit is having the computing step comprises estimating values based on the ratio of standard deviation of the areas of dermal papillae to their mean within the segmentation mask.
[025] In accordance with another embodiment of the present invention, the processing unit includes, but not limited to, a processor and a storage medium; wherein the storage medium stores a computer program adapted to be loaded by the processor and to perform the method steps of any of the above embodiments.
[026] In accordance with another embodiment of the present invention, the processing unit can be optionally connected to a cloud network and communicatively connected to a compatible computation server with the image processing system and provided with compatible interfaces and a set of data processing operation synchronization devices connected to the cloud network.
[027] Further, the exemplary computer system for implementing various embodiments consistent with the present disclosure, which may be used for implementing a system for skin cancer disease detection using machine learning modules 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, 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.
[031] The above-mentioned invention is provided with the preciseness in its real-world applications to provide a system for skin cancer disease detection using machine learning modules. The system can also be used with an online computation server, which can be placed in a cloud network with a real-time data processing and image data detection module for in conjunction with the compatible image data processing and operation synchronization devices connected to the cloud network having a memory unit.
[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 for skin cancer disease detection using machine learning modules, comprising:
an image processing device having a microscopic lens, a light source for generating incident light and a transmission unit for transmitting a microscopy image to an image processing unit;
an optical coherence device having a machine learning based user interface, comprising a light processing unit for emitting and capturing coherence light; and
a processing unit having a microcontroller unit for controlling the microscope and the optical coherence device.
2. The system as claimed in claim 1, wherein the processing unit having the microcontroller unit controls the microscope and the optical coherence device such that the optical coherence device is activated only in consequence of a preceding operation of the microscope and upon fulfilment of an activation criterion for skin cancer disease detection.
3. The system as claimed in claim 1, wherein the image processing device is provided with a mechanical fixture having a flat surface to position or press against the subject's skin to define a distal imaging plane containing a lesion and adapted to obtain image data from light reflected by the distal imaging plane.
4. The system as claimed in claim 1, wherein the processing unit having the microcontroller unit adapted to process the image data with a machine learning based algorithm to obtain metrics and/or one or more classifiers defining the rotational symmetry of the lesion for skin cancer disease detection.
5. The system as claimed in claim 1, wherein the machine learning based algorithm comprises evaluating pixels on a line segment between the center of the lesion image and the border of the lesion image as the line segment rotates about the center of the lesion image for skin cancer disease detection.
6. The system as claimed in claim 1, wherein the machine learning based algorithm comprises estimating values which are statistical measures of local intensity variation in the digital images in each spectral band, which are a function of the texture of the region of interest.
7. The system as claimed in claim 1, wherein the processing unit having the microcontroller unit is having the computing step comprises estimating values based on the ratio of standard deviation of the areas of dermal papillae to their mean within the segmentation mask.
8. The system as claimed in claim 1, wherein the processing unit can be optionally connected to a cloud network and communicatively connected to a compatible computation server with the image processing system and provided with compatible interfaces and a set of data processing operation synchronization devices connected to the cloud network.
9. A method for skin cancer disease detection using machine learning modules, comprising the steps of:
providing, an image processing device having a microscopic lens, a light source for generating incident light and a transmission unit for transmitting a microscopy image to an image processing unit;
providing, an optical coherence device having a machine learning based user interface, comprising a light processing unit for emitting and capturing coherence light; and
providing, a processing unit having a microcontroller unit for controlling the microscope and the optical coherence device.