Advancements in Brain Tumor Detection Using Convolutional Neural Networks for Automated Diagnosis

The identification of brain tumor is crucial in the analysis of medical images. The timely and precise identification of brain tumors has the potential to significantly impact treatment outcomes. The CNN is a viable option for identifying anomalies in images due to its remarkable performance in image classification tasks. This study describes an automated brain tumor detection method that combines Magnetic Resonance Imaging scans with a Convolutional Neural Network. The study utilizes a substantial collection of MRI images, encompassing both tumors and non-tumors. It undergoes meticulous preprocessing and normalization to guarantee the optimal model performance. In the CNN architecture, convolutional and pooling layers provide strong feature extraction, while dropout regularization reduces overfitting. Biologically inspired activation functions, such as Rectified Linear Units, are used. The dataset is divided into two halves for training and evaluation purposes. Adam optimization and Binary CrossEntropy loss are employed in this model. The model demonstrates promise in transforming Neuro imaging Diagnosis by achieving impressive accuracy on the validation set.