# Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models

arXiv: https://arxiv.org/abs/2504.00189 · HTML: https://mlsanity.com/paper/2504.00189/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data](https://mlsanity.com/paper/2412.04111/) (2024)
- [Diffusion-Based Approaches in Medical Image Generation and Analysis](https://mlsanity.com/paper/2412.16860/) (2024)
- [Early Diagnosis of Acute Lymphoblastic Leukemia Using YOLOv8 and YOLOv11 Deep Learning Models](https://mlsanity.com/paper/2410.10701/) (2024)
- [Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study](https://mlsanity.com/paper/2412.20235/) (2024)
- [Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset](https://mlsanity.com/paper/2412.14100/) (2024)

## Abstract (arXiv)

Accurate and quick diagnosis of normal brain tissue Glioma, Meningioma, and Pituitary Tumors is crucial for optimal treatment planning and improved medical results. Magnetic Resonance Imaging (MRI) is widely used as a non-invasive diagnostic tool for detecting brain abnormalities, including tumors. However, manual interpretation of MRI scans is often time-consuming, prone to human error, and dependent on highly specialized expertise. This paper proposes an advanced AI-driven technique to detecting glioma, meningioma, and pituitary brain tumors using YoloV11 and YoloV8 deep learning models. Methods: Using a transfer learning-based fine-tuning approach, we integrate cutting-edge deep learning techniques with medical imaging to classify brain tumors into four categories: No-Tumor, Glioma, Meningioma, and Pituitary Tumors. Results: The study utilizes the publicly accessible CE-MRI Figshare dataset and involves fine-tuning pre-trained models YoloV8 and YoloV11 of 99.49% and 99.56% accuracies; and customized CNN accuracy of 96.98%. The results validate the potential of CNNs in achieving high precision in brain tumor detection and classification, highlighting their transformative role in medical imaging and diagnostics.