Revolutionizing Brain Tumor Detection by Exploring the Environmental Impact and Deep Learning in MRI Segmentation

Maligancy of the brain is a disorder that affects the health of populations worldwide and for diagnosis, calls for complex skills and high-resolution techniques. This work proposes the use of a new strategy for the detection and segmentation of brain tumors with the help of MRI images. Concerning MRI, although the tumors are high in water content and possess hydrogen atoms, they give variations in signal intensity when compared to normal tissues. Thus, the utilization of CNNs for classifying and detecting brain tumors is quite efficient, provided the combined application of the CLAHE algorithm to boost the image contrast, where the outcomes of the classification attained a high accuracy. This chapter reflects on the Sustainable Development Goals in general by contributing to the development of more effective diagnostic tools and, consequently, enhancing the health of individuals. It is in line with the need to have sustainable healthcare systems for the constant and fair diagnosis and treatment of brain tumors regardless of the climate situation.