Advancing Neuro-Oncology with Multiclass Brain Tumour Classification using Pre-trained ResNet50 Model

A crucial task in contemporary medicine is the early and precise diagnosis of brain tumours, which has a significant impact on treatment planning and patient prognosis. The advancements in technology have led towards the early prediction of diseases. Similarly, the deep learning-based ResNet50 model can be used for early brain tumour identification. This is a convolutional neural network, well-known for its skip and depth connections, and presents a viable response to the difficult problems involved in brain tumour detection. Conventional imaging methods, such as magnetic resonance imaging (MRI) and CT scanning, have long been the norm for diagnosing brain tumours. These methods have several drawbacks including high time consumption, high expertise, and cost. The possibility of human mistakes and the incapacity to identify mild irregularities early on is also one of the major concerns. These shortcomings are addressed in the proposed work by the incorporation of ResNet50 into medical imaging, which offers a potent instrument for accurate and timely tumour diagnosis. An overview of the design and processing power of the ResNet50 model for complex visual input is given in this work. The model has been implemented at different numbers of epochs such as 10, 20, 30, 40 and 50. The highest accuracy has been achieved at 95.59%. It goes over how it might improve precision, and evaluate tumour properties (such as size and location). The use of deep learning models in the medical field represents a revolutionary development towards more accurate, consistent, and efficient brain tumour detection. Healthcare providers may enhance patient outcomes by enabling early intervention and customised treatment regimens by using ResNet50's capabilities. This work also highlights the growing importance of deep learning models in the field of medical diagnostics, emphasising its critical function in brain tumour identification.