System And Method For Early Detection Of Breast Cancer

Abstract: BACKGROUND Field of the invention [001] Embodiments of the present invention generally relate to a system for detection of cancers and particularly to a system for the detection of breast cancer in humans. Description of Related Art [002] Cancer is a condition in which the body's cells grow out of control. Breast cancer is a kind of cancer that begins in the breast. Breast cancer starts in the breast and can spread to the lymph nodes or other bodily organs, including the breast. Cancer that has progressed to other organs can potentially spread to the breast. Metastases are the spread of cancer cells from one organ to another. Small cell and non-small cell breast cancers are the two most common kinds of breast cancer (including adenocarcinoma and squamous cell carcinoma). These kinds of breast cancer develop and respond to treatment in various ways. Compared to small cell breast cancer, non-small cell breast cancer is more prevalent. [003] Breast cancer, commonly known as breast carcinoma because it accounts for around 98–99 percent of all breast malignancies, is a malignant breast cancer characterized by uncontrolled cell proliferation in breast tissues. The breast cancer is characterized by an abnormal rate of cell division or death in breast tissue or the airways leading to the breast. Hamartomas, adenomas, and papillomata are examples of benign breast cancers. Breast carcinoma stands out as the most lethal cancer globally, causing nearly 950,000 deaths each year. Enhancing the chances of a favorable outcome and reducing mortality rates is attainable through early detection and accurate diagnosis. Timely identification of the illness plays a crucial role in averting the impact on individuals in the early stages. [004] There is thus a need for a system for early detection of breast cancers that can administer the drawbacks faced by conventional breast cancer detection systems. SUMMARY [005] Embodiments in accordance with the present invention provide a system for an early detection of breast cancer in humans. The system includes a processor located on an application server. The system further includes a storage medium comprising programming instructions executable by the processor. The storage medium includes an image receiving module configured to receive a medical image from a user device. The storage medium further includes a feature extraction module configured to extract features from the received medical image based on a training image set by using a backbone model of a neural network. The storage medium further includes a classification module configured to classify the received medical image based on the extracted features using a machine learning algorithm. The storage medium further includes a cancer prediction module configured to predict a stage of the breast cancer by correlating the classified image with a dataset of pre-stored medical images with various cancer stages, wherein the stage of the breast cancer is selected from a benign stage or a malignant stage. [006] Embodiments in accordance with the present invention further provide a method for early detection of breast cancer in humans. The method includes receiving medical images from a user device; extracting features from the received medical image; classifying the received medical image based on the extracted features; and predicting a stage of the breast cancer by correlating the classified image with a dataset of pre-stored medical images. [007] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application provide a system for an early detection of breast cancer. [008] Next, embodiments of the present application may provide a system for an early detection of breast cancer that is accurate and precise in detection. [009] Next, embodiments of the present application may provide a system for an early detection of breast cancer that provides time-frame for treatment and cure of the breast cancer. [0010] Next, embodiments of the present application may provide a system for an early detection of breast cancer that is cost-effective and user-friendly. [0011] These and other advantages will be apparent from the present application of the embodiments described herein. [0012] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below. BRIEF DESCRIPTION OF THE DRAWINGS [0013] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein: [0014] FIG. 1 illustrates a block diagram depicting a system for early detection of breast cancer, according to an embodiment of the present invention; and [0015] FIG. 2 depicts a flowchart of a method for early detection of breast cancer in humans, according to an embodiment of the present invention. [0016] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, 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). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise. DETAILED DESCRIPTION [0017] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims. [0018] "In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like." [0019] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only. [0020] FIG. 1 illustrates a block diagram depicting a system 100 for early detection of breast cancer, according to an embodiment of the present invention. In an embodiment of the present invention, the system 100 may detect the breast cancer in a user by scanning a medical image provided by the user. According to embodiments of the present invention, the medical image, but not limited to, a Magnetic Resonance Imaging (MRI), a sonography, an X-Ray, and so forth. In a preferred embodiment of the present invention, the medical image may be of a Computed Tomography (CT) scan. Embodiments of the present invention are intended to include or otherwise cover any type of the medical image, including known, related art, and/or later developed technologies. According to embodiments of the present invention, the user may be of any age group and any gender such as, but not limited to, a child, an adolescent, an adult, an old age, and so forth. Embodiments of the present invention are intended to include or otherwise cover any age group of the user. [0021] According to an embodiment of the present invention, the system 100 may comprise a user device 102, a computer application 104, a database 106, a dataset 108, an application server 110, a processor 112, and a storage medium 114. [0022] In an embodiment of the present invention, the user device 102 may be a device used by the user to upload the medical image into the system 100. The user device 102 may further be configured to receive the detection of the breast cancer in the user, in an embodiment of the present invention. According to embodiments of the present invention, the medical image may be, but not limited to, a Magnetic Resonance Imaging (MRI) image, a Computed Tomographic (CT) image, an Ultrasound image, an X-Ray image, and so forth. In a preferred embodiment of the present invention, the medical image may be a thermographic image. Embodiments of the present invention are intended to include or otherwise cover any type of the medical image, including known, related art, and/or later developed technologies. [0023] The user device 102 may be, but not limited to, a personal computer, a consumer device, and alike. Embodiments of the present invention are intended to include or otherwise cover any type of the user device 102 including known, related art, and/or later developed technologies. In an embodiment of the present invention, the personal computer maybe, but not limited to, a desktop, a server, a laptop, and alike. Embodiments of the present invention are intended to include or otherwise cover any type of the personal computer including known, related art, and/or later developed technologies. [0024] Further, in an embodiment of the present invention, the consumer device may be, but not limited to, a tablet, a mobile phone, a notebook, a netbook, a smartphone, a wearable device, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the consumer device including known, related art, and/or later developed technologies. Embodiments of the present invention are intended to include or otherwise cover any type of the user device 102 including known, related art, and/or later developed technologies. [0025] According to an embodiment of the present invention, the user device 102 may comprise software applications such as, but not limited to, a healthcare application, a medical consultation application, an emergency services application, and the like. In a preferred embodiment of the present invention, the user device 102 may comprise a computer application 104 that may be a computer-readable program installed in the user device 102 for executing functions associated with the system 100. [0026] In an embodiment of the present invention, the database 106 may store the dataset 108. The dataset 108 may further comprise medical images that may be stored in the database 106, in an embodiment of the present invention. According to embodiments of the present invention, the database 106 may be for example, but not limited to, a distributed database, a personal database, an end-user database, a commercial database, a Structured Query Language (SQL) database, a non-SQL database, an operational database, a relational database, an object-oriented database, a graph database, a cloud server database, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the database 106 including known, related art, and/or later developed technologies. [0027] Further, the database 106 may be stored in a cloud server, in an embodiment of the present invention. In an embodiment of the present invention, the cloud server may be remotely located. In an exemplary embodiment of the present invention, the cloud server may be a public cloud server. In another exemplary embodiment of the present invention, the cloud server may be a private cloud server. In yet another embodiment of the present invention, the cloud server may be a dedicated cloud server. According to embodiments of the present invention, the cloud server maybe, but not limited to, a Microsoft Azure cloud server, an Amazon AWS cloud server, a Google Compute Engine (GEC) cloud server, an Amazon Elastic Compute Cloud (EC2) cloud server, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the cloud server including known, related art, and/or later developed technologies. [0028] In an embodiment of the present invention, the application server 110 may be a hardware on which the processor 112 may be installed. According to embodiments of the present invention, the application server 110 may be, but not limited to, a motherboard, a wired board, a mainframe, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the application server 110, including known, related art, and/or later developed technologies. [0029] In an embodiment of the present invention, the processor 112 may be located on the application server 110. The processor 112 may be configured to execute the computer-readable instructions to generate an output relating to the system 100. According to embodiments of the present invention, the processor 112 may be, but not limited to, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the processor 112 including known, related art, and/or later developed technologies. [0030] In an embodiment of the present invention, the storage medium 114 may store computer programmable instructions in form of programming modules. The storage medium 114 may be a non-transitory storage medium, in an embodiment of the present invention. In an embodiment of the present invention, the storage medium 114 may store the medical image uploaded by the user through the computer application 104 installed in the user device 102. The storage medium 114 may communicate with the processor 112 and execute computer readable set of instructions present in storage medium 114, in an embodiment of the present invention. [0031] According to embodiments of the present invention, the storage medium 114 may be, but not limited to, a Random-Access Memory (RAM), a Static Random-access Memory (SRAM), a Dynamic Random-access Memory (DRAM), a Read Only Memory (ROM), an Erasable Programmable Read-only Memory (EPROM), an Electrically Erasable Programmable Read-only Memory (EEPROM), a NAND Flash, a Secure Digital (SD) memory, a cache memory, a Hard Disk Drive (HDD), a Solid-State Drive (SSD) and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the storage medium 114, including known, related art, and/or later developed technologies. In an embodiment of the present invention, the storage medium 114 may further comprise an image receiving module 116, a feature extraction module 118, a classification module 120, and a cancer prediction module 122. [0032] In an embodiment of the present invention, the image receiving module 116 may be configured to receive the medical image from the user device 102. The image receiving module 116 may filter and segment the received image and may further transmit the received image to the feature extraction module 118, in an embodiment of the present invention. [0033] In an embodiment of the present invention, the feature extraction module 118 may be configured to extract features from the received medical image. The features may be extracted by using a backbone model of a neural network, in an embodiment of the present invention. In an embodiment of the present invention, the backbone model of the neural network may refer to a feature-extracting network that may be used within the Deep Lab architecture. This feature extractor may be used to encode the user input into a certain feature representation, in an embodiment of the present invention. In an embodiment of the present invention, the features extracted by the feature extraction module 118 may further be transmitted to the classification module 120. [0034] In an embodiment of the present invention, the classification module 120 may be configured to receive the medical image based on the extracted features using a machine learning algorithm. According to embodiments of the present invention, the machine learning algorithm may be, but not limited to, a deep leaning algorithm, a neural network algorithm, and so forth. [0035] In an embodiment of the present invention, the cancer prediction module 122 may be configured to predict a stage of the breast cancer by correlating the classified image with the dataset 108 of pre-stored medical images with cancer stages. The stage of the predicted breast cancer may either be benign or a malignant stage, in an embodiment of the present invention. The user provided medical image through which the breast cancer may be predicted may further be stored in the database 106 for training of dataset 108, in an embodiment of the present invention. [0036] In an embodiment, the cancer prediction module 122 may utilize a neural network or deep learning model to estimate the stage of breast cancer by associating the features derived from a classified image with the dataset 108 containing various pre-stored medical images, each tagged with distinct cancer stages. The anticipated breast cancer stage may encompass classifications as either benign or malignant. Additionally, the user-provided medical image, instrumental in predicting breast cancer stages, may potentially be retained within the database 106, potentially contributing for refining and enhancing the training data (dataset 108) for continual improvement of the model's accuracy. Representing the neural network process under this embodiment, multiple layers of interconnected nodes or neurons may compute transformations and apply activation functions to iteratively learn complex relationships between the input features and the predicted breast cancer stages. Throughout this training phase, the model may continuously adjust its internal parameters, guided by techniques like backpropagation and optimization, aiming to minimize the disparity between predicted and actual cancer stages within the dataset. This adaptive process allows the neural network to improve its ability to predict breast cancer stages based on extracted features from medical images over successive iterations. [0037] FIG. 2 depicts a flowchart of a method 200 for early detection of breast cancer in humans, according to an embodiment of the present invention. [0038] At step 202, the system 100 may receive the medical image from the user device 102. [0039] At step 204, the system 100 may extract features from the received medical image. [0040] At step 206, the system 100 may classify the received medical image. [0041] At step 208, the system 100 may predict the stage of the breast cancer. The stage of the predicted breast cancer may either be the benign stage or the malignant stage. [0042] Embodiments of the invention are described above with reference to block diagrams and schematic illustrations of methods and systems according to embodiments of the invention. It will be understood that each block of the diagrams and combinations of blocks in the diagrams can be implemented by computer program instructions. These computer program instructions may be loaded onto one or more general purpose computers, special purpose computers, or other programmable data processing apparatus to produce machines, such that the instructions which execute on the computers or other programmable data processing apparatus create means for implementing the functions specified in the block or blocks. Such computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the block or blocks. [0043] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. [0044] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims.

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