• Lajwanti Singh
• Garima Singh
• Harshita Jangir
• Khushi Singh
• Neha Kumari

Brain tumors, abnormal growths within the brain, represent a critical healthcare concern due to their potential impact on cognitivefunction and overall,well-being. Cerebrum cancers are of two sorts: benign (non-cancerous) and malignant (cancerous). Early and exactdiscovery is vital for successful treatment. In the field of clinical science, X-ray pictures are broadly utilized in cancer identification. Theapplications of machine learning (ML) for the detection of brain tumors are thoroughly examined in this paper. Surveying a diverse range ofresearch papers, we categorize and compare methodologies, dataset, and performance matrices. For the purpose of automating and increasing theaccuracy of tumor detection, medical imaging data have been subjected to a variety of ML algorithms, including conventional algorithms and sophisticated deep learning models. This comparative study is used to identify various challenges, opportunities, and trends providing valuable insights for practitioners and researcher sselecting productiveML techniques for brain tumor detection.

Convolutional Neural Network (CNN), Deep Learning, Machine Learning (ML), Magnetic Resonance Imaging (MRI), X-ray.

"A Review: Utilizing Machine Learning Algorithms for the Detection and Identification of Brain Tumors", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.e758-e762, April-2024, Available :http://www.jetir.org/papers/JETIR2404483.pdf

"A Review: Utilizing Machine Learning Algorithms for the Detection and Identification of Brain Tumors", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppe758-e762, April-2024, Available at : http://www.jetir.org/papers/JETIR2404483.pdf