Accuracy Enhancement in Detecting Pituitary Tumors Using Deep Learning

For prompt diagnosis and treatment planning, accurate pituitary tumor identification from MRI images is essential in the medical field. An in-depth analysis of the use of the EfficientNet-B0 deep learning architecture for the robotic identification of Pituitary tumors is presented in this paper. The innovative approach distinguished between pituitary tumor and normal MRI images with an outstanding accuracy of 99.48%, thereby increasing life expectancy. The neural network's output was normalized using the SoftMax activation function, and the Adam optimizer was used for effective training. The study used Brain Tumors MRI Dataset of almost 10,000 MRI scans, 5,908 of which showed pituitary tumor cases and 3,066 of which showed healthy brain structures. The obtained results emphasize the need for big, balanced datasets for training robust models as well as the potential of deep learning in improving pituitary tumor detection accuracy.