NEURAL NETWORK-BASED SYSTEM AND METHOD FOR BRAIN TUMOR DETECTION IN MEDICAL IMAGING

Abstract

The present invention relates to a system and method for detecting brain tumors in medical imaging data using neural networks. Specifically, the invention employs convolutional neural networks (CNNs) or similar deep learning architectures to analyze magnetic resonance imaging (MRI) or computed tomography (CT) scans of the brain. The neural network is trained on a dataset of annotated medical images to learn features indicative of brain tumors, enabling it to accurately classify new images as either tumor-positive or tumor-negative. The system provides a reliable and automated solution for brain tumor detection, aiding clinicians in making timely and accurate diagnoses.

Specification

Description:FIELD OF INVENTION
[001] The invention represents a system and method for detecting brain tumors in medical imaging data using neural networks. Specifically, the invention employs convolutional neural networks (CNNs) or similar deep learning architectures to analyze magnetic resonance imaging (MRI) or computed tomography (CT) scans of the brain.

BACKGROUND OF THE INVENTION
[002] Neural networks are a class of machine learning algorithms inspired by the structure and function of the human brain. They are designed to recognize patterns, make decisions, and solve complex problems through a process that mimics how the brain processes information. The concept of neural networks has evolved significantly over the past several decades, leading to advancements in artificial intelligence (AI) and deep learning.
[003] Brain tumors are one of the most serious and life-threatening conditions, significantly impacting patient health and survival rates. The prognosis for individuals diagnosed with brain tumors heavily depends on early detection and prompt intervention. Delays in diagnosis can lead to the progression of the disease, making treatment more challenging and reducing the likelihood of successful outcomes. Consequently, early and accurate detection is vital to improving patient survival rates and quality of life.
[004] Traditionally, brain tumor detection has relied on the manual inspection of medical images-such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans-by radiologists and medical experts. While these imaging technologies provide detailed views of brain structures, identifying tumors within these images requires a high level of expertise. Radiologists must carefully analyze each scan, searching for abnormalities that may indicate the presence of a tumor.
[005] The manual review of medical images is a labor-intensive and time-consuming task. A single patient may undergo multiple scans, each generating a large number of images that need to be meticulously examined. This can lead to delays in diagnosis, especially in healthcare settings with high patient volumes or limited radiology staff.
[006] Despite the expertise of radiologists, the interpretation of medical images can be subjective. Different radiologists may arrive at different conclusions when examining the same set of images. Factors such as fatigue, workload, and experience level can influence the accuracy of tumor detection, increasing the risk of false negatives (missing a tumor) or false positives (identifying a tumor when there is none).
[007] Brain tumors vary widely in size, shape, location, and appearance, which can make them difficult to detect, especially in the early stages when they may be small or resemble other benign conditions. The complexity of the brain's anatomy and the subtlety of early tumor signs further complicate the detection process, requiring radiologists to exercise significant caution and judgment. Advances in imaging technology have led to higher resolution scans and the generation of larger datasets. While this provides more detailed information, it also increases the burden on radiologists who must sift through vast amounts of data to identify potential tumors. This growing volume of imaging data exacerbates the time and effort required for accurate diagnosis.
[008] The development of automated systems for brain tumor detection represents a significant advancement in medical imaging technology. By addressing the limitations of traditional methods, these systems have the potential to transform the way brain tumors are diagnosed, ultimately leading to faster, more accurate and more consistent detection. This innovation is particularly important in the context of increasing imaging data and the need for timely intervention in the treatment of brain tumorsPatent literature 1, US8588486B2 relates to system, method, and apparatus includes a computer readable storage medium with a computer program stored thereon having instructions that cause a computer to access a first anatomical image data set of an imaging subject acquired via a morphological imaging modality, access a functional image data set of the imaging subject acquired via a functional imaging modality, register the first anatomical image data set to the functional image data set, segment the functional image data set based on the functional image data set, define a binary mask based on the segmented functional image data set, and apply the binary mask to the first anatomical image data set to construct a second anatomical image data set and an image based thereon. The second anatomical image data set is substantially free of image data of the first anatomical image data set correlating to an area outside the region of physiological activity.
[009] Patent literature 2, EP3928284A1 relates to a system and method to analyze image data. The images data may be used to assist in determine the presence of a feature in the image. The feature may include a bubble. A method of selecting a region for determining a presence of a bubble in an image, comprising: determining a tracked location of an instrument; accessing a current image of a subject; registering an image space of the current image to a subject space of the subject; determining a location of the instrument within the image space based on the determined tracked location of the instrument; determining a region of interest relative to the determined location of the instrument within the image space; and analyzing the current image todetermine if a bubble is present in the current image within the region of interest.
[010] Patent literature 3, CN107980008B relates to medical image processing apparatus includes a first image acquirer that acquires a plurality of first images of an object to be treated, each of the plurality of first images being taken by a radiographic imaging apparatus at a respective time of a plurality of times; a second image acquirer that acquires a second image of the object to be treated, the second image being taken by the radiographic imaging apparatus at a time different from a plurality of times at which the plurality of first images are taken; a feature processor for determining a plurality of positions of a first feature of the object to be treated from the plurality of first images, the feature processor determining a position of a second feature corresponding to the first feature in the object to be treated from the second image; a calculator to establish a first enclosed space including the plurality of locations of the first feature; and a determiner that determines whether the position of the second feature is within the first enclosed space, the determiner outputting a determination signal according to a result of the determination.


OBJECTS OF THE INVENTION

[011] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
[012] It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative therapy for measuring height and weight.
[013] An object of the present disclosure is to provide a system and method for the automated detection of brain tumors in medical imaging data using neural networks, specifically convolutional neural networks (CNNs).
[014] Another object of the present disclosure is to provide a system that automates the detection of brain tumors in medical imaging data, reducing the reliance on manual inspection by radiologists.
[015] Another object of the present disclosure is to system that utilizes neural networks, particularly convolutional neural networks (CNNs), to achieve high accuracy in identifying brain tumors, minimizing false positives and false negatives.
[016] Another object of the present disclosure is to enhance the speed and efficiency of the diagnostic process, enabling rapid analysis of MRI and CT scans for timely diagnosis and intervention.
[017] Still another object of the present disclosure is to system that can be scaled to handle large volumes of medical imaging data, making it suitable for various healthcare settings.
[018] Yet another object of the present disclosure is to integrate the system into existing medical imaging workflows to assist clinicians in making more informed and accurate decisions, ultimately improving patient outcomes.
[019] Other objects and advantages of the present disclosure will be more apparent from the following description and accompanying drawing which is not intended to limit the scope of the present disclosure.

SUMMARY OF THE INVENTION
[020] The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concept of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.
[021] The present invention is generally directed to a system and method for the automated detection of brain tumors in medical imaging data using neural networks, specifically convolutional neural networks (CNNs).
[022] An embodiment of the present invention provides a system that automates the detection of brain tumors in medical imaging data, reducing the reliance on manual inspection by radiologists.
[023] An embodiment of the present invention provides systems that utilize neural networks, particularly convolutional neural networks (CNNs), to achieve high accuracy in identifying brain tumors, minimizing false positives and false negatives.
[024] Another embodiment of the present invention provides a system that enhances the speed and efficiency of the diagnostic process, enabling rapid analysis of MRI and CT scans for timely diagnosis and intervention.
[025] Another embodiment of the present invention provides system that can be scaled to handle large volumes of medical imaging data, making it suitable for various healthcare settings.
[026] Another embodiment of the present invention provides the system into existing medical imaging workflows to assist clinicians in making more informed and accurate decisions, ultimately improving patient outcomes.

BRIEF DESCRIPTION OF DRAWINGS

[027] Figure 1 shows the system illustrating the neural network based system for brain tumor detection in medical imaging.

DETAILED DESCRIPTION OF THE INVENTION

[028] The following description is of exemplary embodiments only and is not intended to limit the scope, applicability or configuration of the invention in any way. Rather, the following description provides a convenient illustration for implementing exemplary embodiments of the invention. Various changes to the described embodiments may be made in the function and arrangement of the elements described without departing from the scope of the invention.
The invention pertains to a sophisticated system and method for the automated detection of brain tumors in medical imaging data using advanced neural network techniques, particularly convolutional neural networks (CNNs). This system is designed to enhance the accuracy, speed, and efficiency of diagnosing brain tumors by analyzing MRI and CT scans. The invention comprises several key components and processes, each playing a vital role in the overall functionality of the system:
1. Data Acquisition Module:
The system begins with a data acquisition module that collects high-quality medical imaging data, specifically MRI or CT scans of the brain. These images are sourced from patients and are annotated with labels indicating the presence or absence of brain tumors. The annotations serve as ground truth data, which is essential for training the neural network.
2. Preprocessing Module:
Once the imaging data is acquired, it undergoes a preprocessing phase. This module applies various image processing techniques to enhance the quality of the medical images. Key steps in this phase include:
Image Normalization: Standardizing the intensity values of the images to ensure consistency across the dataset.
Noise Reduction: Removing artifacts and noise that could interfere with the neural network's ability to detect tumors accurately.
Geometric Transformation: Aligning and resizing the images to a uniform format that is compatible with the neural network architecture.
This preprocessing ensures that the images fed into the neural network are of optimal quality, thus improving the accuracy and reliability of the system.
3. Neural Network Architecture:
At the core of the invention is a deep learning model, typically a convolutional neural network (CNN), specifically designed for analyzing medical images. The neural network architecture comprises several layers, each performing different functions:
Convolutional Layers: Extract features from the input images by applying various filters to detect edges, textures, and other important patterns associated with brain tumors.
Pooling Layers: Reduce the dimensionality of the data by summarizing the output of the convolutional layers, which helps in retaining essential features while minimizing computational complexity.
Fully Connected Layers: Perform the classification task by mapping the extracted features to the final output, which indicates whether a brain tumor is present (tumor-positive) or absent (tumor-negative).
The neural network is designed to learn from the training data, identifying patterns that are indicative of brain tumors, and subsequently applying this knowledge to classify new, unseen images.
4. Training Module:
The training module is responsible for teaching the neural network how to detect brain tumors. This process, known as supervised learning, involves feeding the preprocessed and labeled medical images into the neural network. During training, the network adjusts its parameters (e.g., weights and biases) through backpropagation to minimize the loss function, which measures the difference between the network's predictions and the ground truth labels.
The training process involves several key elements:
Loss Function: A mathematical function that quantifies the error between the predicted output and the actual label. Common loss functions include cross-entropy loss for classification tasks.
Optimization Algorithms: Techniques like stochastic gradient descent (SGD) or Adam optimization are used to update the network's parameters iteratively, ensuring that the model converges to an optimal solution.
Training continues until the neural network reaches a satisfactory level of accuracy, where it can reliably classify images with minimal error.
5. Validation Module:
To ensure that the trained neural network generalizes well to new data, the system includes a validation module. This module tests the network on a separate set of images that were not used during training. Validation helps to evaluate the network's performance, checking for issues like overfitting (where the model performs well on training data but poorly on new data) or underfitting (where the model fails to capture the underlying patterns in the data).
Metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve are used to assess the network's effectiveness in detecting brain tumors.
6. Testing Module:
After validation, the system undergoes a final testing phase, where it is evaluated on a completely new set of medical images that the network has never seen before. This phase simulates real-world conditions, providing an accurate measure of how the system will perform in clinical settings. The testing module calculates various performance metrics, confirming the system's readiness for deployment.
7. Inference Module:
Once the neural network is trained, validated, and tested, it is deployed for real-time or near-real-time classification of new medical images. The inference module allows clinicians to input new MRI or CT scans into the system, which then quickly and accurately determines whether the images indicate the presence of a brain tumor. This module is designed for high efficiency, ensuring that diagnoses can be made rapidly, facilitating timely treatment decisions.
8. Advantages and Applications:
The system provides several advantages, including automation, which reduces the burden on radiologists and minimizes the risk of human error. It also offers high accuracy and efficiency, enabling rapid and reliable tumor detection. The system is scalable, making it suitable for various healthcare environments, from large hospitals to smaller diagnostic centers.
This neural network-based system for brain tumor detection is poised to significantly impact the field of neuroimaging, providing a powerful tool for early detection and diagnosis, ultimately improving patient outcomes through timely intervention.

While considerable emphasis has been placed herein on the specific features of the preferred embodiment, it will be appreciated that many additional features can be added and that many changes can be made in the preferred embodiment without departing from the principles of the disclosure. These and other changes in the preferred embodiment of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
, Claims:We Claim:

1. A system for brain tumor detection in medical imaging data comprising:
a. a data acquisition module configured to acquire magnetic resonance imaging (MRI) or computed tomography (CT) scans of the brain from patients along with corresponding annotations indicating the presence or absence of tumors;
b. a preprocessing module configured to preprocess the medical images to enhance image quality, remove noise, and standardize the data format;
c. a neural network architecture comprising a convolutional neural network (CNN) or similar deep learning model designed to analyze medical images and classify them as tumor-positive or tumor-negative based on learned features;
d. a training module configured to train the neural network on a dataset of labeled medical images using supervised learning techniques; and
e. an inference module configured to deploy the trained neural network to classify new unseen medical images as either tumor-positive or tumor-negative.
2. The system as claimed in claim 1, wherein the neural network architecture further comprises multiple convolutional layers, pooling layers, and fully connected layers for feature extraction and classification.
3. The system as claimed in claim 1, wherein the preprocessing module further comprises image normalization, intensity normalization, and geometric transformation techniques.
4. The system as claimed in claim 1, wherein the training module further comprises optimization algorithms such as stochastic gradient descent (SGD) or Adam optimization for minimizing the loss function during training.
5. The system as claimed in claim 1, wherein the inference module further comprises real-time or near-real-time processing capabilities for rapid tumor detection in medical imaging data.

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