edef77a7-6a92-4522-9e28-8116ed12128f.pptx

Early brain stroke detection using machine learning

1. ABSTRACT Stroke is one of the leading causes of death and disability worldwide. Early detection and identification of stroke are crucial for improving patient outcomes and reducing the long-term impact of the condition. . This project aims to develop innovative methods for stroke identification using advanced technologies such as machine learning, artificial intelligence, and medical imaging analysis. By integrating multiple data sources and leveraging novel techniques, this solution enhances the accuracy and speed of stroke diagnosis. The approach offers a significant improvement over traditional methods, contributing to timely medical interventions and potentially saving lives.

2. INTRODUCTION Stroke, a medical emergency caused by a disruption of blood flow to the brain, can lead to severe neurological damage, long-term disability, or death. Conventional methods of stroke detection, such as CT scans and MRI, while accurate, often face challenges in real-time diagnosis and early intervention. Delays in identifying strokes can significantly impact a patient’s recovery and survival chances. Hence, there is a growing need for innovative solutions that can enhance the speed and accuracy of stroke diagnosis. The advent of advanced computational techniques such as machine learning and artificial intelligence has paved the way for groundbreaking improvements in the healthcare sector. In the case of stroke identification, these technologies can analyze complex medical imaging data with greater precision and speed. They can assist medical professionals in making faster decisions, enabling immediate treatment and reducing the likelihood of permanent brain damage. In this project, we focus on developing innovative techniques for stroke detection by employing deep learning algorithms and medical image processing. The integration of these technologies will allow for more accurate and timely detection of strokes, facilitating faster medical responses. This work aims to revolutionize the current diagnostic practices and significantly improve patient outcomes.

3. MOTIVATION Stroke is a critical medical condition where every second counts, and early identification is key to reducing damage. The need for innovative approaches in stroke identification stems from the limitations of traditional methods, which may not always provide real-time, accurate results. Advances in medical imaging and data science present an opportunity to develop faster and more accurate diagnostic tools. Machine learning and artificial intelligence can process complex medical data more effectively, leading to quicker interventions. This project is motivated by the potential to save lives and improve healthcare through cutting-edge technologies.

4. PROBLEM STATEMENT Input: Medical imaging data (e.g., CT scans, MRI scans) and patient health records. Process: Preprocessing the medical images to remove noise and enhance relevant features. Applying image segmentation techniques to isolate areas of interest, such as the brain. Extracting critical features from the images to identify stroke-related anomalies. Using machine learning models to classify the images as stroke-positive or stroke-negative. Integrating clinical data with image analysis to improve diagnostic accuracy. Evaluating the model’s performance and optimizing it for better accuracy and speed. Output: A diagnostic result indicating the presence or absence of a stroke, facilitating early medical intervention and treatment.

5. SCOPE The project focuses on developing a system that combines medical image processing and machine learning to identify strokes in real-time. The solution will target multiple types of stroke, including ischemic and hemorrhagic strokes, providing a comprehensive diagnostic tool. It aims to significantly reduce the time required for diagnosis and increase the accuracy compared to traditional methods. This innovation will not only benefit healthcare professionals but also improve patient outcomes by enabling faster and more accurate treatment. The project will also explore future applications in remote stroke diagnosis and telemedicine platforms.

6. OBJECTIVES Develop a machine learning-based model for the early identification of strokes using medical imaging data. Improve the accuracy and speed of stroke diagnosis by integrating image processing techniques. Enhance feature extraction methods to better identify stroke-related anomalies in medical images. Implement a classification algorithm to differentiate between stroke-positive and stroke-negative cases. Evaluate and optimize the system's performance to ensure it can be used in real-time clinical settings. Explore the potential for integrating the system into telemedicine for remote stroke diagnosis.

7. LITERATURE SURVEY S,NO TITLE AUTHOR NAME PUBLICATION METHODOLOGY MERITS DEMERITS 1 "An Adaptive Hybrid Brain–Computer Interface for Hand Function Rehabilitation of Stroke Patients" ianqiang Su, Jiaxing Wang, Weiqun Wang 2024 Combines EEG (motor intention) and EMG (muscle activity) for hand rehabilitation. Adapts exercises based on real-time feedback. Integrates neural signals with physical therapy. - Real-time adaptive feedback. - Tailors exercises to individual needs. Complex system setup. - Requires patient training. - High equipment cost

8. LITERATURE SURVEY S,NO TITLE AUTHOR NAME PUBLICATION METHODOLOGY MERITS DEMERITS 2 An Adaptive Brain-Computer Interface to Enhance Motor Recovery After Stroke Rui Zhang, Chushan Wang, Shenghong He, Chunli Zhao, Keming Zhang 2023 Adaptive BCI records neural activity, guides rehab exercises, adjusts task difficulty in real-time to promote neuroplasticity. Encourages neuroplasticity. - Real-time task adjustment. - Efficient motor control recovery. High computational resources. - Time-consuming customization. - Not effective for all patients.

9. LITERATURE SURVEY S,NO TITLE AUTHOR NAME PUBLICATION METHODOLOGY MERITS DEMERITS A Quantitative Microwave Imaging Approach for Brain Stroke Classification Based on Generalized Tikhonov Regularization Sayyed Saleh Mousavi, Mohammadsaeed Majedi 2023 Uses microwave imaging with Tikhonov regularization to classify strokes as ischemic or hemorrhagic, enhancing image reconstruction quality. Non-invasive stroke classification. - Quick diagnosis. - High image accuracy. Limited clinical validation. - High computational complexity. - Specialized equipment needed.

10. LITERATURE SURVEY S,NO TITLE AUTHOR NAME PUBLICATION METHODOLOGY MERITS DEMERITS 4 Time Series Shapelet -Based Movement Intention Detection Toward Asynchronous BCI for Stroke Rehabilitation Thapanan Janyalikit, Chotirat Ann Ratanamahatana 2022 Shapelet -based algorithm for detecting movement intention using EEG time-series data, supporting asynchronous BCIs. Real-time movement detection. - Reduces need for continuous patient input. - Improves limb rehabilitation. Computationally intensive. - Limited movement detection. - Risk of false positives.

11. LITERATURE SURVEY S,NO TITLE AUTHOR NAME PUBLICATION METHODOLOGY MERITS DEMERITS 5 Low-Intensity Focused Ultrasound Neuromodulation for Stroke Recovery: A Novel Deep Brain Stimulation Approach Mahmut Martin Yüksel , Gregoire Courtine , Shiqi Sun 2023 Non-invasive LIFU stimulates deep brain structures for motor recovery and neuroplasticity post-stroke. Non-invasive. - Targets deep brain areas. - Promotes neuroplasticity and motor recovery. Limited long-term studies. - Equipment availability. - Potential unintended neural effects.

12. LITERATURE SURVEY S,NO TITLE AUTHOR NAME PUBLICATION METHODOLOGY MERITS DEMERITS 6 Experimental Validation of the DBIM- TwIST Algorithm for Brain Stroke Detection and Differentiation Olympia Karadima, Panagiotis Kosmas, Pan Lu, Ioannis Sotiriou 2022 Validates DBIM- TwIST algorithm for stroke detection in a multi-layered head phantom model, improving stroke differentiation accuracy. High accuracy in stroke differentiation. - Effective in phantom models. - Detailed brain analysis Requires further clinical validation. - Complex setup. - Limited real-world testing.

13. Data preprocessing Testing Set Training set Output Flow chart

14. DATASET Dataset Origin: The dataset was obtained from Kaggle's repository, aimed at stroke prediction using CT scan images. This is part of a larger initiative to assist in medical image analysis. Dataset Content: Geography: The dataset includes images from various regions around the world. Time Period: The specific time period of data collection is not specified. Unit of Analysis: Individual CT scan images are used to predict stroke occurrence. Variables: The dataset contains several key variables, including: Image data: High-quality CT scan images of brain scans. Class labels: Categories such as Stroke (presence of stroke) and Non-Stroke (no stroke). Image resolution and size: The resolution of each image varies based on the scan. Other diagnostic features (e.g., hemorrhagic stroke, ischemic stroke), which may be inferred from image patterns. Image Details: Format: The images are provided in a common format, such as JPEG or PNG, for easy use in machine learning models. Pre-labeling: The images are already labeled for training and testing machine learning models, making them suitable for direct use in classification algorithms. Dataset Link: You can access the dataset here ​( Kaggle )​( Kaggle ).

15. Architecture Diagram

16. Dataset Collection : This is the first step where data is gathered from multiple sources, including hospitals, clinics, and research databases. It might consist of medical records, imaging data (like MRI or CT scans), and patient demographic information. Preprocessing : In this stage, the raw data collected is cleaned and transformed to ensure quality. Preprocessing tasks may include normalization of data, handling missing values, and removing any outliers or noise that could affect the analysis. Feature Extraction : This involves identifying and extracting relevant features from the cleaned data. Features could include patient history, imaging characteristics, vital signs, and risk factors (like age, hypertension, diabetes, etc.) that are important for stroke identification. Classification : In this phase, machine learning or deep learning algorithms are applied to classify the extracted features. The goal is to determine whether the data indicates a stroke or not, thus making predictions that can guide further medical decisions. Result : Finally, the classification results are outputted. This output provides healthcare professionals with predictions and insights that can assist in timely and accurate stroke identification, potentially leading to better patient outcomes.

17. SYSTEM REQUIREMENTS Hardware Requirements: System : Pentium IV 2.4 GHz. Hard Disk : 40 GB. Monitor : 15 VGA Colour Ram : 512 Mb Minimum   Software Requirements: Operating system : Windows XP/ Windows 7 or More Software Tool : IDLE(python 3) Coding Language : Python

18. REFERENCES Su, J., Wang, J., & Wang, W. (2024). An adaptive hybrid brain–computer interface for hand function rehabilitation of stroke patients. [Journal Name] , Volume (Issue), pages. Zhang, R., Wang, C., He, S., Zhao, C., & Zhang, K. (2023). An adaptive brain-computer interface to enhance motor recovery after stroke. [Journal Name] , Volume (Issue), pages. Mousavi, S. S., & Majedi , M. (2023). A quantitative microwave imaging approach for brain stroke classification based on the generalized Tikhonov regularization. [Journal Name] , Volume (Issue), pages. Janyalikit , T., & Ratanamahatana , C. A. (2022). Time series shapelet -based movement intention detection toward asynchronous BCI for stroke rehabilitation. [Journal Name] , Volume (Issue), pages. Yüksel , M. M., Courtine , G., & Sun, S. (2023). Low-intensity focused ultrasound neuromodulation for stroke recovery: A novel deep brain stimulation approach for neurorehabilitation? [Journal Name] , Volume (Issue), pages. Karadima , O., Kosmas, P., Lu, P., & Sotiriou , I. (2022). Experimental validation of the DBIM- TwIST algorithm for brain stroke detection and differentiation using a multi-layered anatomically complex head phantom. [Journal Name] , Volume (Issue), pages. Wu, K. (2024). Dynamic reconfiguration of brain functional network in stroke. [Journal Name] , Volume (Issue), pages.