• Siva Naga Prasad Mannem
• Bobbili Rutish
• Maddisetti Kundhana Lalitha Naga Sri
• Kammindi Chaitanya MahaLakshmi
• Alajangi Manoj

This study offers a novel method for detecting brain tumor disorders by combining deep learning (DL) and machine learning (ML) approaches. Brain tumors are a major global health concern, and better patient outcomes are largely dependent on early and precise identification. Conventional tumor detection techniques depend on radiologists manually interpreting medical imaging data, which is laborious and prone to human error. To extract important properties like tumor size, form, texture, and spatial relationships from medical imaging images (MRI and CT), the suggested method makes use of machine learning algorithms like Support Vector Machines (SVM) and Random Forests. These characteristics enable the algorithm to accurately classify images as either tumor-positive or tumor-negative. The accuracy and resilience of the detection process are further improved by the incorporation of deep learning models, specifically Convolutional Neural Networks (CNNs), which automatically learn hierarchical features from raw image data .By automating the tumor detection process, the suggested system overcomes the drawbacks of conventional techniques and provides a number of benefits, such as increased accuracy, shortened diagnostic times, and reliable outcomes. The system's exceptional performance is demonstrated by the experimental results, which show 96% classification accuracy, 94% precision, 95% recall, and 94.5% F1-score. This study demonstrates the revolutionary potential of incorporating machine learning and deep learning into medical diagnostics, offering a scalable and effective method for detecting brain tumors.

Detection, Machine Learning, Brain Tumor, Random Forest, MRI & CT, CNN.

"Enhancing Brain Tumor Detection with Automated Feature Extraction Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.e743-e756, March-2025, Available :http://www.jetir.org/papers/JETIR2503501.pdf

"Enhancing Brain Tumor Detection with Automated Feature Extraction Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppe743-e756, March-2025, Available at : http://www.jetir.org/papers/JETIR2503501.pdf