NEUROSENTINEL PRODIGY


EOI: 10.11242/viva-tech.01.06.011

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Citation

Shaad Shaikh, Vansh Shah, Deven Randive, Prof. Saniket Kudoo, "NEUROSENTINEL PRODIGY", VIVA-IJRI Volume 1, Issue 6, Article 11, pp. 1-13, 2023. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

Brain tumor detection is a crucial task in the realm of medical diagnostics, bearing significant implications for patient care and outcomes. This research paper embarks on a comprehensive exploration of the development and deployment of an advanced brain tumor detection system. The methodological framework is multifaceted, commencing with the assembly of a diverse and extensive dataset of brain imaging scans. Subsequently, the data undergoes rigorous preprocessing, including noise reduction and image enhancement, to optimize the quality and fidelity of the scans. The heart of the system lies in the utilization of deep learning, particularly a convolutional neural network (CNN), which leverages the robust features extracted from the preprocessed data to distinguish between brain scans indicative of tumors and those that are not. Model training is augmented by the introduction of a validation set, allowing for finetuning to achieve optimal performance. Testing the trained model on an entirely separate and previously unseen dataset substantiates its real-world utility, providing critical insights into its robustness and accuracy. The practical implementation of the system involves seamless integration into a real- time processing platform, enabling rapid analysis of incoming brain imaging data. This operational phase includes the establishment of predefined thresholds, effectively reducing false alarms and ensuring that only the most probable cases are flagged for review by medical professionals.

Keywords

Brain Tumor, CNN, Medical.

References

  1. Tariq Sadad, Amjad Rehman, Asim Muni,r Tanzila Saba, Usman Tariq, Noor Ayesha, Rashid Abbasi “Brain tumor detection and multi-classification using advanced deep learning techniques” 2020, National Institutes of Health.
  2. Pallavi Tiwari, Bhaskar Pant, Mahmoud M. Elarabawy, Mohammed Abd-Elnaby, Noor Mohd, Gaurav Dhiman and Subhash Sharma “CNN Based Multiclass Brain Tumor Detection Using Medical Imaging” 2022, Hindawi.
  3. Tanzila Saba, Ahmed Sameh Mohamed, Mohammad El-Affendi, Javeria Amin, Muhammad Sharif “Brain tumor detection using fusion of hand crafted and deep learning features” 2020, ELSEVIER.
  4. Md Khairul Islam, Md Shahin Ali, Md Sipon Miah, Md Mahbubur Rahman, Md Shahariar Alam, Mohammad Amzad Hossain “Brain tumor detection in MR image using superpixels, principal component analysis and template-based K- means clustering algorithm” 2021, ELSEVIER.
  5. Chirodip Lodh Choudhury, Chandrakanta Mahanty, Raghvendra Kumar “Brain Tumor Detection and Classification Using Convolutional Neural Network and Deep Neural Network” 2020, IEEE.
  6. Sarmad Maqsood, Robertas Damaševi cius and Rytis Maskeliunas “Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM” 2022, MEDICINA.
  7. Mesut Toğaçar, Burhan Ergen, Zafer Cömert “BrainMRNet: Brain Tumor Detection using Magnetic Resonance Images with a Novel Convolutional Neural Network Model” 2019, ELSEVIER.
  8. Shtwai Alsubai, Habib Ullah Khan, Abdullah Alqahtani, Mohemmed Sha, Sidra Abbas and Uzma Ghulam Mohammad “Ensemble Deep Learning dor Brain Tumor Detection.” 2022, FRONTIERS.
  9. Wadhah Ayadi, Wajdi Elhamzi, Imen Charf, Mohamed Atri “Deep CNN for Brain Tumor Classifcation” 2021, SPRINGER.
  10. Neelum Noreen, Sellappan Palaniappan, Abdul Qayyum, Iftikhar Ahmad, Muhammad Imran, and Muhammad Shoaib “A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor” 2020, IEEE.
  11. Emrah Irmak “Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework” 2020, SPRINGER.
  12. Francisco Javier Díaz-Pernas, Mario Martínez-Zarzuela, Míriam Antón-Rodríguez and David González-Ortega “A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network” 2021, MDPI.
  13. Nyoman Abiwinanda, Muhammad Hanif, S. Tafwida Hesaputra, Astri Handayani, and Tati Rajab Mengko “Brain Tumor Classification Using Convolutional Neural Network” 2021, SPRINGER.
  14. Miss Krishna Pathak, Mr. Mahekkumar Pavthawala, Miss Nirali Patel, Mr. Dastagir Malek, Prof. Vandana Shah, Prof. Bhaumik Vaidya “Classification of Brain Tumor Using Convolutional Neural Network” 2019, IEEE.
  15. Masoumeh Siar, Mohammad Teshnehlab “Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm” 2019, ResearchGate.