• Amulya Mattukoyya
• Dr.U.Sathish Kumar
• Dr.K.Chaitanya

An aberrant clump of cells forming tissue is called a tumor, feeding on healthy cells and continuously growing. Brain tumors are particularly significant, as they contribute to a high mortality rate among cancer types. The accurate classification of brain tumors through Magnetic Resonance Imaging (MRI) is crucial for effective treatment. It serves as a critical initial step in identifying abnormal tissue and enabling efficient patient recovery. In this study, we present an in-depth analysis of brain tumor detection and classification. We assess the performance of a deep learning approach using a pre-trained ResNet50 model through transfer learning, coupled with dimensionality reduction techniques. Additionally, we leverage machine learning algorithms to predict brain tumor cells. The pre-trained ResNet50 model is applied to a dataset of MRI brain tumor images. This classification process aids medical professionals and researchers in the early-stage detection of brain tumors, offering valuable insights for timely intervention

BrainTumorDetection, Medical Imaging, MRI Images, Transfer Learning, Dimensionality Reduction, Deep Learning, machine learning algorithm.

"FEATURE REDUCTION FOR ENHANCED BRAIN TUMOR DETECTION WITH TRANSFER LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.h536-h545, October-2023, Available :http://www.jetir.org/papers/JETIR2310667.pdf

"FEATURE REDUCTION FOR ENHANCED BRAIN TUMOR DETECTION WITH TRANSFER LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. pph536-h545, October-2023, Available at : http://www.jetir.org/papers/JETIR2310667.pdf