From Imaging to Insights: A Review of Techniques for Brain Tumor Analysis

An abnormal cell mass in the brain is called a brain tumor. Benign or malignant (cancerous) brain tumors are both possible. Malignant brain tumors are a common and dangerous tumor that can shorten life expectancy if not detected in time. After a brain tumor is discovered, creating a successful treatment plan requires proper classification of the tumor. The dataset consists of MRI images of the human brain. It includes brain MRI images with and without tumors. Next, preprocessing techniques like filtering, blurring, and cropping are applied to the data. Training and testing sets make up the dataset. To enhance the data, a number of arbitrary transformations are employed. A CNN (Convolutional Neural Network) model is fed the pre-trained dataset. The tumor’s presence is then ascertained by the model. In the event of a tumor, it is further divided into three categories. Meningiomas, pituitary tumors, and gliomas are among them. Furthermore, 3D CNNs are used to segment cranial anomalies, which allows for a more detailed understanding of tumor regions. Early detection of cranial anomalies is critical for improving patient outcomes and survival rates, making it important in the field of medical diagnosis.