• H. S. Palsokar
• Dr. Z. I. Khan

Brain tumor detection is a critical task in medical diagnosis, where timely and accurate identification can significantly improve treatment outcomes and patient survival rates. Traditional diagnostic methods rely heavily on manual analysis of MRI scans, which is time-consuming, prone to human error, and requires expert radiological interpretation. This study presents an effective approach for brain tumor detection using deep learning techniques. The proposed methodology leverages convolutional neural networks (CNNs) to automatically extract spatial features from MRI images, enabling precise classification and localization of tumor regions. Preprocessing techniques such as noise reduction, image normalization, and segmentation are integrated to enhance the quality of input data. The model is trained and evaluated on publicly available MRI datasets to ensure robustness and generalizability. Experimental results demonstrate high accuracy, sensitivity, and specificity in detecting tumor-affected regions, outperforming traditional machine learning approaches. This work highlights the potential of deep learning as a powerful, automated, and non-invasive tool for reliable brain tumor detection, contributing to improved clinical decision-making and early diagnosis.

Brain Tumor Detection, Deep Learning, Convolutional Neural Networks (CNN), MRI Imaging, Medical Image Processing, Image Segmentation, Classification

"Deep Learning Techniques for Accurate Detection of Brain Tumors from MRI Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.e261-e271, November-2025, Available :http://www.jetir.org/papers/JETIR2511433.pdf

"Deep Learning Techniques for Accurate Detection of Brain Tumors from MRI Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppe261-e271, November-2025, Available at : http://www.jetir.org/papers/JETIR2511433.pdf