Brain tumor detection using neural networks by classifying brain mri images

The brain, regarded as a vital center for the human body, unfortunately may harbor serious conditions such as brain tumors, making accurate diagnosis of these diseases crucial for enhancing treatment efficacy and survival rates. Artificial intelligence (AI) intervenes at this juncture and can be employed to improve the classification of complex diseases. In this context, Convolutional Neural Networks (CNNs) have generally demonstrated significant success in image classification. CNNs have advanced image processing and comprehension of visual data, enhancing the recognition of patterns and objects within images. These networks employ a series of layers, including convolutional and pooling layers, to progressively extract information from images. Utilizing deep learning techniques, CNNs can recognize intricate patterns and details, classify objects and individuals, detect objects, and distinguish objects within images with high accuracy. Particularly in classifying brain tumors using magnetic resonance imaging (MRI), CNNs have proven effective in accurately classifying and analyzing medical images. This technology can be utilized to extract critical information from images and precisely determine the type and location of tumors, thereby assisting in guiding doctors towards appropriate treatment and improving the treatment success of brain tumor patients. As CNNs' ability to learn patterns and enhance performance becomes fundamental across various fields, continual development and implementation persist for better and more precise comprehension of visual data. In this study, three different artificial neural network models, namely VGG16, EfficientNetB3, and ResNet50, were employed. A large dataset consisting of pre-classified brain tumor MRI images was utilized. This dataset was split into training and testing data to evaluate the models' performance. The results of the study were highly promising, as the EfficientNetB3 model achieved a classification accuracy of 99.21%, demonstrating the potential for this model to distinguish tumors with exceptional precision. Utilizing artificial intelligence in the classification of brain tumors can enhance diagnostic accuracy and aid in directing patients towards the most suitable treatment. Moreover, it can contribute to the development of new techniques to improve the diagnosis and treatment of brain diseases. This study is considered a step towards a bright future in classifying brain tumors using intelligent technology.