Health Care Diagnostics Using Deep Learning: Brain Tumors Detection

Brain tumors are indeed serious medical conditions that can have devastating effects on individuals. Early diagnosis of brain tumors is crucial for improving the survival rates of patients. However, the manual identification of brain tumors is complex and time-consuming, highlighting the need for advanced diagnostic techniques to enhance the efficiency and accuracy of detection. Those needs resulted in the integration of Artificial Intelligence (AI) and deep learning in automating brain tumor detection This paper proposed two deep learning models based on 2D CNN architecture containing a data augementation function that uses rotation, shift and horizontal or vertical flip combined with brightness change to produce extra samples, then adjust sampels size to fit the proposed model. As a result, the two proposed approach provides amazing overall performance, The first model was 94.975 % accurate during training, 97.101 % accurate during validation and 96.601 % accurate during testing, The second model was 95.22 % accurate during training, 95.169 % accurate during validation and 95.145 % accurate during testing.