Deep Learning-Based Hybrid Framework Utilizing OpenCV and CNN for Automated Brain Tumor Detection and MRI Image Classification

Brain tumors constitute a significant public health issue worldwide, affecting not only individuals patients but also healthcare infrastructures. Recently, the prevalence of brain and other central nervous system cancers has shown alarming trends. World health statistics show that each year, tens of thousands of people in the United States are diagnosed with brain cancer, with gliomas accounting for a significant portion of these cases. Gliomas exhibit a wide spectrum of severity and prognostic outcomes, positioning brain cancer as one of the most significant challenges faced by the medical community. This complexity, coupled with the of-ten poor long-term survival rates for brain cancer patients, under-scores the critical need for advanced diagnostic tools and method-ologies. By combining the advantages of VGG-16 and ResNet-SO, the proposed hybrid model improves the performance of existing brain tumor detection models, resulting in more reliable and ac-curate feature learning. In contrast to conventional models that depend on a single CNN architecture, the hybrid model makes use of ResNet-5O's residual learning to capture intricate patterns and VGG-16's capacity to extract detailed features. This combination improves the model's capacity to identify cancers more accurately. Utilizing both architectures also reduces overfitting and improves generalization across various datasets. The hybrid model provides a more complete diagnostic tool by classifying different tumor types (meningioma, pituitary tumors, and gliomas), whereas many existing models concentrate on binary classification. Through the division of brain images into smaller sections for in-depth examination, the integration of these advanced techniques enables more precise tumor detection. After being trained on three publicly accessible brain MRI datasets, the model exhibits exceptional resilience and applicability, with an average of 97% for ac-curacy, precision, recall, and Fl-score. This excellent performance highlights the model's capacity to distinguish between tissues im-pacted by tumors and those that are not, thereby offering physi-cians a useful tool. Patients with brain cancer may benefit greatly from this model's increased precision and effectiveness in terms of early detection and treatment results. This method is useful in clinical settings since it speeds up the tumor diagnosis process and lessens the need for radiologists' manual skill. The practical solutions this research offers to improve early diagnosis and increase survival rates in cases of brain cancer go beyond its scholarly achievements. In the end, this paradigm gives medical professionals a more precise and effective way to rec-ognize and categorize brain tumors, which enhances patient out-comes and care.