Detection of Brain Tumors Using Optimized Features in Clustering Techniques for Enhanced Model Development with Accuracy

The growth of cells in the brain or nearby tissues, known as brain tumors, they may be termed benign(non-cancerous) or malignant(cancerous) and can cause various symptoms depending on their location and size. Brain tumor, both benign and malignant cause significant clinical challenges due to their complexity and the diverse range of symptoms they produce depending on their location, size, and type. Tumor classification has traditionally relied on histopathological examination, but molecular insights are becoming crucial in improving accuracy and treatment strategies. Clustering techniques particularly DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identify molecular subtypes in brain tumor datasets, specifically genetic expression data from the GSE50161 dataset. The goal is to improve the detection of Brain Tumor patterns ultimately contributing to better diagnostic, prognostic, and treatment strategies for the patients. Identifying distinct molecular subtypes through genetic expression data can assist in creating personalized treatment plans for patients. By categorizing tumors more accurately, clinicians can choose therapies that target specific molecular mechanisms leading to better outcome Ultimately leading to enhanced accuracy in brain tumor detection.