• K. Teja
• K.S.M. Eshwar
• K. Vamsi Naik
• K. Vasanth
• S.K.Umamaheswari

These days, Brain tumor detection has become up as a fashionable causality inside the realm of fitness care. A brain tumor happens while extraordinary cells form inside the brain this is there is no control over the growth of the cells. The manner of photograph segmentation is adopted for extracting odd tumor area within the brain. In the MRI (magnetic resonance imaging), segmentation of brain tissue holds very significant for you to identify the presence of outlines concerning the mind tumor. With appropriate use of accurate data mining class strategies, early prediction of any sickness may be correctly done. Inside the clinical area, the techniques of ML (gadget studying) and statistics mining holds a enormous stand. Majority of which is adopted efficiently. The studies examine listing of risk elements which might be being traced out in mind tumor surveillance systems. Additionally, the technique proposed assures to be especially efficient and particular for mind tumor detection, class and segmentation. To reap this specific automated or semiautomatic strategies are needed. The studies propose an automated segmentation technique that is based upon CNN (convolution neural networks). With the aid of incorporating this unmarried technique, segmentation and category is accomplished. CNN (an ML technique) from NN (neural networks) wherein in it has layered based for outcomes type. Various levels worried in the proposed mechanisms. Through using the DM (data mining) techniques, huge family members and patterns from the records may be extracted. The strategies of ml (system getting to know) and facts mining are being successfully employed for mind tumor detection and prevention at an early level. This neural network offers us the chance of the way likely the lifestyles of tumor within the brain, and had skilled over magnetic resonance snap shots, the diversity of images changed into 1500 healthy brains and 1500 with tumor. The dataset contains of 3000 magnetic resonance images. The version gave us splendid results of predicting the lifestyles of a tumor which reached 95.42% in validation statistics and as much as 93.67% on check information.

Brain Tumor, Magnetic Resonance Imaging (MRI), Machine Learning, Convolutional Neural Networks (CNN), Brain Tumor Detection, Feature Extraction, Cloud Computing.

"Brain Tumor Detection Using Machine Learning Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.f55-f63, April-2024, Available :http://www.jetir.org/papers/JETIR2404507.pdf

"Brain Tumor Detection Using Machine Learning Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppf55-f63, April-2024, Available at : http://www.jetir.org/papers/JETIR2404507.pdf