Tumor is a condition where an abnormal growth of body tissue takes place inside the body; these can be cancerous or non-cancerous. Among all the types of cancers, Brain tumors are very rare but deadly. These tumors are difficult to cure as compared to the tumors in other body parts, as they exist within the protected location inside the brain. However, generally, CT scan Computer Tomography scan and MRIs Magnetic Resonance Imaging are used for detection of tumors and for the treatment surgery, radiation and chemotherapy are done but it has been observed that these treatments effects badly on the normal functionality of the brain, also CT Scans and MRIs can only detect tumors in their later stages. Thus, to save one’s life early detection of the brain tumor is very crucial. Machine Learning is an emerging technology these days which can be a revolutionary milestone in medical science hence, a survey has been conducted on the various available machine-learning techniques so that early detection of brain tumors become possible and the techniques, which are still unnoticed, can grab the attention giving benefit to the mankind.

As per the reports given by World health organization, to identify accurately about brain tumor identification or detection on the basis of what is its type, in which location it is existing inside the brain and what is the grade of tumor. Finding the brain tumor location is crucial for further for performing segmentation process and rest of the other steps involved in the classification. Dataset taken from openly accessible resources majorly contains information related to the grade, location of existence and type of brain tumor it belongs to. The main objective behind this is to decrease the complexity by new techniques. In this survey, we have come across various such proposed methodologies    by using various machine learning techniques or classifiers with the final goal to achieve good accuracy and to identify the brain tumor in the human body as early as possible.

Keywords: Machine Learning, Brain tumor, Classification Techniques