Maximizing patient results hinges on their early diagnosis of brain tumors, alongside proper treatment administration. The development of sophisticated medical imaging tools has led to DL being one of the most critical automated methods for the recognition and the classification of a brain tumor from the MRI images. This study seeks to test the efficiency of certain deep learning models in detecting brain tumors from a region in Himachal Pradesh, India. The dataset includes MRI images for different kinds of brain tumors to train and test the model to detect and classify the tumors accurately. The MRI images are processed with convolutional neural networks to automate the detection and segmentation of the tumors, thus minimizing the amount of work that has to be done manually

CNN, DL, MRI, CT, 3-D, Deep Learning, SVM

As stated previously, there is scope for research within the limitation of region-specific datasets for brain tumor detection in Himachal Pradesh, which can be bridged with the creation of AI-generated datasets relevant to the demographic, healthcare infrastructure, and environmental characteristics of the region. Through employing machine learning methodologies, especially in the area of synthetic data generation, it is possible to design valid datasets that incorporate health history, medical activities, and lifestyle attributes of the people in Himachal Pradesh. Not only would this improve the performance of the brain tumor detection models, but it would also increase their applicability to the local populace so that the diagnostic tools developed are useful and relevant in a rural and mountainous setting. The consideration of local environmental, genetic and socioeconomic variables for AI-generated datasets would enhance the understanding of brain tumor risk factors in Himachal Pradesh

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