Brain Stroke Detection Using Machine Learning

Registration ID: IJNRD_217476

Published ID: IJNRD2404343

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Anmol Kaur, Vishal Kumar Singh, ANAMIKA LARHGOTRA

Brain Tumor Segmentation (BTS), Magnetic Resonance Imaging (MRI), Deep Learning (DL), Convolutional Neural Networks (CNNs)

Stroke is a severe medical condition that requires prompt diagnosis and treatment to prevent disastrous consequences. In this piece of work, we present a unique approach to detect brain strokes using machine learning techniques. We employ a variety of machine learning techniques, including support vector machines (SVM), decision trees, and deep learning models, to efficiently identify and categorize stroke cases from medical imaging data. Machine learning techniques are applied for stroke identification after preprocessing processes are critical in improving the quality of the medical pictures and lowering noise. We examine many machine learning architectures and methods, such as random forests, k- nearest neighbours (KNNs), and convolutional neural networks (CNNs), and evaluate their efficacy in accurately detecting strokes from brain imaging data. The models are trained and validated using an extensive dataset of labeled brain imaging scans, enabling thorough performance assessment. The identification accuracy of stroke cases is further enhanced by applying transfer learning from pre-trained models and data augmentation techniques. Furthermore, post-processing methods such as morphological operations and feature extraction are utilized to improve the overall detection performance by fine-tuning the identified stroke regions. Our findings reveal that machine learning algorithms perform promisingly when it comes to identifying brain strokes from medical imaging data, especially deep learning models like CNNs. The suggested method provides accurate and efficient stroke detection, which may help medical practitioners diagnose and treat stroke patients more quickly. As a result, our research concludes that machine learning algorithms are a useful diagnostic tool for brain strokes, offering medical professionals a useful resource in clinical situations.

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Anmol Kaur, Vishal Kumar Singh, & ANAMIKA LARHGOTRA (April-2024). Brain Stroke Detection Using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), d355-d359. https://ijnrd.org/papers/IJNRD2404343.pdf

Volume 9 Issue 4, April-2024

Pages : d355-d359

Paper Reg. ID: IJNRD_217476

Published Paper Id: IJNRD2404343

Research Area: Computer Engineering 

Author Type: Indian Author

Country: Fatehgarh churian, Punjab, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404343.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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