Artificial intelligence for brain tumor diagnosis: a review of models, techniques, and clinical implications

The use of artificial intelligence (AI) and deep learning has made a big difference in finding and sorting out brain tumors. This paper talks about various AI methods, like specially made Convolutional Neural Networks (CNNs) and already trained models such as ResNet50, EfficientNet, DenseNet121, and InceptionV3. These models have shown great results when used with MRI images to detect tumors. Moreover, techniques such as Grad-CAM, SHAP, and LIME assist in making AI models more transparent, thereby enabling doctors to comprehend the output of the model more easily. Improved image quality is attained by processes such as equalization, homomorphic filtering, and segmentation to aid in obtaining more precise results while detecting and classifying tumors. In addition, models that combine several AI models together, or combine CNNs with conventional approaches such as Support Vector Machines (SVMs) and Random Forests, have been found to perform well in detecting various types of tumors. Despite all these developments, challenges such as limited datasets, variations in types of tumors, and regulations that must be adhered to before these models are employed in actual hospitals still exist. To deal with these issues and help use AI in brain tumor treatment, techniques like data expansion and transfer learning can help. The paper shows how AI is changing the way brain tumors are diagnosed early, how treatment is planned, and how care is provided, which helps patients get better results.