FORM 2
THE PATENT ACT 1970 (39 OF 1970)
AND
The patent rules, 2003 COMPLETE SPECIFICATION
(See section 10: rule 13)
TITLE OF INVENTION
NeuroSense: Automated Brain Tumor Detection System Using Machine
Learning and Neural Networks
APPLICANTS
Institute of Technology & Management, Gwalior
INVENTORS
Name Nationality Address
Dr. Deepak Gupta Indian Department of CSE, Institute of
Technology & Management, Gwalior
Dr. Rishi Soni Indian Department of CSE, Institute of
Technology & Management, Gwalior
Chandra Prakash
Bhargav
Indian Department of CSE, Institute of
Technology & Management, Gwalior
Arun Agrawal Indian Department of CSE, Institute of
Technology & Management, Gwalior
Deshdeepak
Shrivastava
Indian Department of IT, Institute of
Technology & Management, Gwalior
Dr. Pradeep Yadav Indian Department of CSE, Institute of
Technology & Management, Gwalior
Gaurav Dubey Indian Department of CSE, Institute of
Technology & Management, Gwalior
Ratandeep Singh Indian Department of CSE, Institute of
Technology & Management, Gwalior
Rajkumar Rajoria Indian Department of Electronics and
Communication, Institute of
Technology & Management, Gwalior
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PREAMBLE TO THE DESCRIPTION
COMPLETE
Following specification particularly describes the invention and the manner in
which it is to be performed.
Technical field of invention:
The present invention pertains to the technical field of medical diagnostic systems
and more specifically to an automated brain tumor detection system employing
machine learning and neural networks. This invention encompasses the integration
of medical imaging techniques, particularly magnetic resonance imaging (MRI) and
computed tomography (CT) scans, with advanced computational methods based on
machine learning and artificial intelligence (AI). The primary objective of the
invention is to enhance the accuracy and efficiency of brain tumor detection, catering
to the needs of medical professionals and ultimately improving patient outcomes in
the domain of neurology and healthcare at large.
Background:
In the rapidly evolving landscape of healthcare technology, the quest for more
precise and efficient diagnostic tools has been an ongoing pursuit. Among the
myriad medical conditions that require early detection, brain tumors stand as a
formidable challenge. These growths within the brain's tissue can vary widely in
size, location, and malignancy, making their identification a complex task.
Traditionally, the diagnosis of brain tumors has rested upon the discerning eye of
radiologists and neurologists who painstakingly analyze magnetic resonance
imaging (MRI) and computed tomography (CT) scans. While this approach has
proven effective, it has its limitations, including the potential for human error,
variations in interpretation, and the time-intensive nature of manual image analysis.
Furthermore, the demand for timely and accurate brain tumor detection has been
growing as the medical community increasingly recognizes the critical role early
intervention plays in improving patient outcomes. The development of automated
systems for medical image analysis has emerged as a promising solution to address
these challenges. In this context, the present invention, NeuroSense, represents a
pioneering leap forward. It harnesses the power of machine learning and neural
networks, bringing the precision of computational analysis to the intricate world of
medical imaging. NeuroSense is designed to automatically identify and classify
brain tumors within MRI and CT scans, offering the potential to significantly reduce
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diagnostic time, mitigate the risks associated with human error, and empower
medical professionals with an invaluable tool for informed decision-making.
NeuroSense builds upon decades of research and innovation in the fields of machine
learning, computer vision, and medical imaging. It embodies the synergy between
medicine and technology, enabling the seamless integration of cutting-edge AI
algorithms into clinical practice. By providing an automated and objective
assessment of brain images, NeuroSense holds the promise of not only expediting
the diagnostic process but also improving the accuracy of brain tumor detection.
Moreover, it can serve as an indispensable resource for healthcare institutions, aiding
in the allocation of resources and optimizing patient care strategies.
In the next sections, this patent application will delve into the technical aspects of
NeuroSense, detailing its architecture, the machine learning algorithms at its core,
and the steps involved in automated brain tumor detection. Through this invention,
the future of neurology diagnostics is poised for a transformative shift, one where
technology and human expertise converge to enhance the quality of patient care and
elevate the capabilities of medical professionals in the complex and critical field of
brain tumor detection.
Objective of the invention
The primary objective of the invention, NeuroSense, is to provide an advanced
and automated system for the detection of brain tumors using cutting-edge
machine learning and neural network technologies. This innovative system seeks
to address the following key objectives:
Enhanced Diagnostic Accuracy: NeuroSense is designed to significantly improve
the accuracy of brain tumor detection in magnetic resonance imaging (MRI) and
computed tomography (CT) scans. By leveraging machine learning algorithms
and neural networks, it aims to reduce the likelihood of false negatives and false
positives, thereby enhancing diagnostic precision.
Efficiency in Healthcare: The invention aims to expedite the brain tumor diagnosis
process, allowing medical professionals to rapidly assess patient images and make
informed decisions. This increased efficiency is expected to lead to timely
treatment planning and better patient outcomes.
Automation and Assistive Technology: NeuroSense intends to serve as a valuable
tool for radiologists and neurologists by automating the initial screening and
detection of brain tumors. It does not replace the expertise of medical
professionals but rather assists them in their diagnostic tasks.
Interpretability and Trust: The invention strives to provide interpretable results,
ensuring that medical practitioners can understand and trust the system's findings.
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This objective is critical in a healthcare context where decisions impact patient
care.
Accessibility: NeuroSense is designed with user-friendliness in mind, making it
accessible to a wide range of healthcare professionals. The objective is to create
an intuitive interface that does not require extensive training for effective use.
Advancement in Neurology: Ultimately, the invention seeks to advance the field
of neurology by introducing a state-of-the-art technology that empowers medical
experts and optimizes their capabilities in the detection and characterization of
brain tumors.
Mathematical Model:
Let's define some key terms and variables:
I: The input image (MRI or CT scan).
T: The true label indicating the presence or absence of a brain tumor (0 for
no tumor, 1 for tumor).
P(T|I): The probability of the image I containing a brain tumor, as predicted
by the neural network.
θ: The model parameters (weights and biases) of the neural network.
L(θ): The loss function that quantifies the error between the predicted
probability P(T|I) and the true label T.
D: The dataset of labeled brain images used for training.
N: The number of images in the dataset.
α: The learning rate, a hyperparameter controlling the step size in the
gradient descent algorithm for training.
Now, let's describe the mathematical model:
Neural Network Prediction:
NeuroSense employs a neural network to predict the probability P(T|I) that an
input image I contains a brain tumor. This can be represented using a neural
network architecture with multiple layers, including input, hidden, and output
layers. The output layer uses a sigmoid activation function to produce the
probability value:
𝑃( 𝑇 ∣ 𝐼; 𝜃 ) = 𝜎(𝑊𝑇 ∗ 𝐼 + 𝑏)
where:
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 P(T∣I;θ) is the predicted probability.
 σ is the sigmoid activation function.
 W is the weight matrix of the output layer.
 b is the bias vector of the output layer.
Training Objective:
The objective during training is to minimize a loss function L(θ), which measures
the error between the predicted probabilities and the true labels in the training
dataset D. A common choice for binary classification is the binary cross-entropy
loss:
𝐿(𝜃) = −𝑁1Σ𝑖 = 1𝑁[𝑇(𝑖) ∗ 𝑙𝑜𝑔(𝑃(𝑇 = 1
∣ 𝐼(𝑖); 𝜃)) + (1 − 𝑇(𝑖)) ∗ 𝑙𝑜𝑔(1 − 𝑃(𝑇 = 1 ∣ 𝐼(𝑖); 𝜃))]
Gradient Descent Optimization:
To train the neural network and find the optimal parameters θ, gradient descent
can be employed. The parameters are updated iteratively using the gradient of the
loss function with respect to θ:
𝜃 ← 𝜃 − 𝛼 ∗ ∇𝐿(𝜃)
Where ∇L(θ) is the gradient of the loss function with respect to θ, calculated using
backpropagation.
This mathematical model provides a simplified representation of the core
components and processes involved in NeuroSense.
Summary of the invention:
“NeuroSense" is a groundbreaking automated brain tumor detection system designed
to revolutionize the field of medical imaging and neurology. Leveraging state-ofthe-
art machine learning and neural network technologies, NeuroSense offers a
transformative solution for accurate and efficient brain tumor diagnosis.
Key Features and Contributions:
Advanced Medical Imaging Analysis: NeuroSense is equipped with a sophisticated
neural network architecture that can analyze magnetic resonance imaging (MRI)
and computed tomography (CT) scans with unparalleled precision. It employs
cutting-edge techniques from computer vision and machine learning to detect and
classify brain tumors within these medical images.
Enhanced Diagnostic Accuracy: The primary objective of NeuroSense is to
significantly improve diagnostic accuracy in the detection of brain tumors. By
automating the analysis process, it reduces the potential for human error and
enhances the ability to identify both common and rare tumor types.
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Efficiency and Timeliness: NeuroSense accelerates the diagnostic timeline by
providing rapid results. Healthcare professionals can quickly assess images,
enabling timely treatment planning and better patient outcomes. The system
streamlines the workflow of radiologists and neurologists, allowing them to focus
on critical decision-making.
Interpretability and Trust: A distinctive feature of NeuroSense is its ability to
provide interpretable results. Medical practitioners can understand and trust the
system's findings, fostering collaboration between technology and healthcare
expertise.
User-Friendly Interface: NeuroSense offers an intuitive and user-friendly interface,
ensuring accessibility for healthcare professionals with varying levels of expertise.
It does not require extensive training, making it an efficient and adaptable tool in
clinical settings.
Advancement in Neurology: Beyond its immediate diagnostic benefits, NeuroSense
contributes to the advancement of neurology by bridging the gap between cuttingedge
technology and medical practice. It empowers medical experts, optimizes their
diagnostic capabilities, and aligns with the evolving demands of modern healthcare.
Brief description of drawings
Figure 1 shows a system design diagram representation of Implementation of
invention.
Figure 2 shows a sequence diagram representation of Implementation of
invention.
Figure 3 shows a Class diagram representation of Implementation of invention.
Figure 4 shows a Flow chart diagram representation of Implementation of
invention.
We Claims:
Claim 1: A method for automated brain tumor detection using a neural network
system, comprising:
A receiving step of obtaining medical images, including magnetic
resonance imaging (MRI) or computed tomography (CT) scans, as input
data.
A preprocessing step to prepare the obtained medical images by resizing,
normalizing, and augmenting the data for input into the neural network.
An image analysis step using a neural network architecture, said neural
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network comprising multiple layers with weight parameters and
employing convolutional neural network (CNN) techniques, to
automatically detect brain tumors within the medical images.
A classification step to classify the medical images as having no brain
tumor (0) or having a brain tumor (1) based on the output of the neural
network.
Claim 2: The method according to claim 1, further comprising:
An interpretability step to provide explanations for the automated brain
tumor detection results, allowing medical professionals to understand the
basis of the classification.
Claim 3: The method according to claim 1, wherein the neural network is trained on
a labeled dataset of medical images, optimizing weight parameters through
an iterative process of gradient descent to improve diagnostic accuracy.
Claim 4: The method according to claim 1, wherein the receiving step further
includes obtaining metadata associated with the medical images, such as
patient information and image acquisition parameters, and integrating this
metadata into the analysis process.
Claim 5: A computer-readable storage medium comprising computer-executable
instructions for performing the method of automated brain tumor detection
as claimed in claim 1, when executed on a computer system.
Claim 6: A computer-implemented automated brain tumor detection system,
comprising:
6.1. A data input module configured to receive medical images, including
MRI or CT scans, as input data.
6.2. A neural network module comprising a neural network architecture
with weight parameters and employing CNN techniques, configured to
preprocess and analyze the received medical images to automatically
detect brain tumors.
6.3. A classification module configured to classify the medical images as
having no brain tumor (0) or having a brain tumor (1) based on the output
of the neural network.
Claim 7: The system according to claim 6, further comprising:
7.1. An interpretability module configured to provide explanations for the
automated brain tumor detection results, enabling healthcare professionals
to understand the basis of the classification.
Claim 8: The system according to claim 6, wherein the neural network module is
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trained on a labeled dataset of medical images, optimizing weight
parameters through an iterative process of gradient descent to improve
diagnostic accuracy.
Claim 9: The system according to claim 6, wherein the data input module further
includes a metadata integration component to obtain and incorporate
metadata associated with the medical images into the analysis process.