International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 02 | Feb 2024
p-ISSN: 2395-0072
www.irjet.net
BRAIN TUMOUR DETECTION AND CLASSIFICATION Aryan Galande1, Suraj Mohite2, Pavan Thorat3, Prof. Rajani Jadhav4 1Aryan Galande: Student, Dept. of Computer Engineering, PICT Pune. 2Suraj Mohite: Student, Dept. of Computer Engineering, PICT Pune.
3Pavan Thorat: Student, Dept. of Computer Engineering, PICT Pune.
4Prof. Rajani Jadhav: Professor, Dept. of Computer Engineering, PICT Pune. ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Cancer is one of the largest health problems
location, and size of brain tumours can be seen with the aid of these imaging investigations. Biopsy: To obtain a conclusive diagnosis, a biopsy often entails taking a tiny sample of the tumour tissue to be examined under a microscope. This aids in identifying the exact type of tumour and whether it is malignant. Blood Tests: New studies are investigating the possibilities of liquid biopsies, which entail testing blood samples for the presence of DNA mutations linked to brain cancer or tumor-specific markers. Developments in Imaging: The capacity to accurately map brain tumours and evaluate their effects on surrounding brain structures has been enhanced by developments in medical imaging technology, including diffusion tensor imaging and functional magnetic resonance imaging. Options for Treatment: Surgery, radiation therapy, chemotherapy, immunotherapy, targeted therapy, or a mix of these may be used to treat brain cancer. Options for less invasive treatment are frequently made possible by early detection. Research and Innovation: Developing novel treatments for brain tumours and enhancing the precision and noninvasiveness of diagnostic methods are the two main objectives of continuous efforts in the field of brain cancer detection. Early Diagnosis and Prognosis: Early detection of brain cancer is critical to improving patient quality of life and raising the chance of a successful treatment plan. Moreover, accurate prognosis is necessary to tailor treatment plans to individual patients.
the world faces today, and early detection is key to better patient outcomes. Traditional tumour detection methods sometimes involve intrusive procedures and have disadvantages. The integration of Artificial Intelligence (AI) and Machine Learning (ML) has led to breakthrough developments in the field of medical diagnostics in recent years, particularly in the identification of malignant tumours. This research provides a thorough analysis of the application of AI and ML techniques in the early diagnosis and detection of cancer. Keywords—Cancer Detection, Tumor Detection, Artificial Intelligence, Machine Learning, Medical Imaging, Radiology, Healthcare, Early Diagnosis. Key Words: Cancer Detection, Tumour Detection, Artificial Intelligence, Machine Learning, Medical Imaging , Radiology, Healthcare, Early Diagnosis .
1.INTRODUCTION A crucial area of medical research and clinical practice is the detection of cancer in the brain. This field focuses on the identification and diagnosis of tumours that originate inside the central nervous system, which includes the brain and spinal cord. Brain tumours, commonly referred to as brain cancer, can be malignant (cancerous) or benign (noncancerous). Early brain cancer detection is essential for successful treatment and better patient outcomes. The following are some important details about brain cancer detection: Different Brain Tumour Types: Brain tumours are categorized according to their origin, location, and level of malignancy. Whereas secondary brain tumours originate from cancer that has progressed to the brain from other parts of the body, primary brain tumours originate in the brain or spinal cord. Brain cancer symptoms can vary greatly and include weakness, eyesight issues, altered behaviour or cognitive function, chronic headaches, and seizures. Due to the mild and nonspecific nature of these symptoms, early identification is frequently difficult. Diagnostic Tools: A mix of diagnostic tools and medical imaging techniques, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans, are commonly used to detect brain cancer. The presence,
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1.1 Literature Survey In a 2021 study by Q.D. Buchlak et al., the applications of machine learning in neuroimaging for glioma detection and classification were explored. This research offers valuable insights into the use of AI specifically for glioma diagnosis, a common and aggressive type of brain tumor. Understanding the specific methods and algorithms employed for glioma diagnosis is essential for comprehending the broader landscape of brain tumor detection. M.K. Abd-Ellah et al.'s 2019 review provides a comprehensive overview of brain tumor diagnosis from MRI images, with a focus on the practical implications. This study offers a holistic perspective on the challenges and opportunities associated with using MRI for brain
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