This paper presents a method for detecting brain tumors using an optimal convolutional neural network (CNN). The study aims to provide an efficient and reliable approach for early detection of brain tumors, which is critical for effective treatment. The method involves several image processing steps, including resizing, data separation, reshaping, flattening, and CNN-based classification. The research uses MRI images of the brain, which are processed through a series of steps to prepare them for CNN analysis. These steps include resizing the images to a fixed size, separating the data into training, validation, and test sets, reshaping the data to match the input requirements of the CNN, and flattening the data into a one-dimensional format. The CNN then processes the data to detect the presence of a brain tumor. The CNN algorithm uses convolutional layers to extract features and patterns from the input images. These features are then passed through pooling layers to reduce the dimensionality of the data and minimize overfitting. The results are then passed through fully connected layers for classification. The model's ability to accurately predict the presence of a tumor is its main advantage. The study shows that CNN and deep learning algorithms have shown remarkable promise in the identification of brain tumors. These methods can significantly improve the accuracy and efficiency of brain tumor detection, reducing the time and effort required for manual analysis. The results indicate that CNN algorithms can achieve high accuracy in detecting brain tumors, with some models achieving over 95% accuracy. The use of CNN in brain tumor detection has the potential to transform the field of radiology by enabling early diagnosis and treatment, thereby improving patient outcomes.This paper presents a method for detecting brain tumors using an optimal convolutional neural network (CNN). The study aims to provide an efficient and reliable approach for early detection of brain tumors, which is critical for effective treatment. The method involves several image processing steps, including resizing, data separation, reshaping, flattening, and CNN-based classification. The research uses MRI images of the brain, which are processed through a series of steps to prepare them for CNN analysis. These steps include resizing the images to a fixed size, separating the data into training, validation, and test sets, reshaping the data to match the input requirements of the CNN, and flattening the data into a one-dimensional format. The CNN then processes the data to detect the presence of a brain tumor. The CNN algorithm uses convolutional layers to extract features and patterns from the input images. These features are then passed through pooling layers to reduce the dimensionality of the data and minimize overfitting. The results are then passed through fully connected layers for classification. The model's ability to accurately predict the presence of a tumor is its main advantage. The study shows that CNN and deep learning algorithms have shown remarkable promise in the identification of brain tumors. These methods can significantly improve the accuracy and efficiency of brain tumor detection, reducing the time and effort required for manual analysis. The results indicate that CNN algorithms can achieve high accuracy in detecting brain tumors, with some models achieving over 95% accuracy. The use of CNN in brain tumor detection has the potential to transform the field of radiology by enabling early diagnosis and treatment, thereby improving patient outcomes.