Detecting Brain Cancer Using Explainable AI

The accurate and early diagnosis of brain tumors that is one of the deadly cancers may save many humans. Proposed research focused on primary types of tumors detection (i.e. gliomas, meningiomas, and pituitary tumors). Further, the research is meticulous in fine-tuning the model’s parameters — including the loss function, optimizer, learning rate, epochs, and batch size — to optimize performance and achieve high accuracy in tumor detection. While the findings of this work mark a significant advancement in medical imaging and brain tumor detection, it acknowledges the prevailing limitations, such as the constraints imposed by hardware resources and the challenges of model generalization across diverse and more extensive datasets. Recognizing these limitations, the work sets the stage for future research, emphasizing the exploration of larger and more heterogeneous datasets, the adoption of more sophisticated deep learning models, with the aim of enhancing the precision, reliability, and clinical applicability of brain tumor detection methodologies. In essence, this research not only contributes to the field of medical imaging analysis but also represents a transformative step towards the realization of more accurate, timely, and personalized healthcare solutions, powered by the advancements in artificial intelligence and machine learning.