Early Detection of Brain Tumors: A Comprehensive Study on MRI-Based Diagnosis Using a Combination of Convolutional Deep Learning and Machine Learning Techniques


  • Patel Rahulkumar Manilal, D. J. Shah


Brain Tumors, Glioma, Meningioma, Pituitary, Magnetic Resonance Imaging


Brain tumors are one of the global public health problems that affect people of every age category, and early detection of the tumor is extremely important for the life of an individual. The complicated and diverse nature of brain tumor symptoms makes their detection a challenge, necessitating improved imaging techniques for reliable diagnosis. This study applies deep convolutional learning combined with machine learning techniques to delve into early brain tumor identification using MRI-image-based classification. The model presented in this study uses an ensemble model that combines random forest and support vector machine which provides improved and more accurate early brain tumor detection. This has been proven as the ensemble model achieves an improved 97% recall rate, a 96% F-score, a 98.25% accuracy rate, and 98.89% precision in early brain tumor identification. Furthermore, the model's ability to correctly detect the type of brain tumor in the input image also highlights its ability for brain tumor classification and identification.


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How to Cite

Patel Rahulkumar Manilal. (2024). Early Detection of Brain Tumors: A Comprehensive Study on MRI-Based Diagnosis Using a Combination of Convolutional Deep Learning and Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 01–10. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6380



Research Article