Brain Tumor Detection Using the Inception Deep Learning Technique

This article presents a modern brain tumor recognition method heavily based on deep neural network techniques. Although huge datasets are challenging to train, deep convolutional neural networks have excelled in many computer vision tasks. The deadliest disease in humans, according to some estimates, is a brain tumor. Anormal cells in the body can or may not spread rapidly depending on the location of the tumor and its predicted growth rate. The dataset consists of brain tumor images. To enhance the performance of the image, a data augmentation technique is used to avoid overfitting issues. Overfitting is the process by which a network learns a function with a very high variance in order to perfectly model the training data. Access to big data is limited in many application domains, including medical image analysis. The main focus of this work is data augmentation, a data-space solution to the problem of limited data. Any techniques that increase the size of the dataset can be loosely referred to as data augmentation. For instance, the image can zoom in or out and save the result, alter the brightness of the image, or rotate it. The obtained data are trained with k-fold, a data partitioning technique that enables making the most of the dataset to create a more comprehensive model. Finally, an inception framework facilitates the ability to train much deeper networks than those previously used. Three image augmentation algorithms are covered in this article: data augmentation, the k-fold model, and the inception v3 model that classifies the images by minimizing overfitting issues. Lastly, there is an application page that contains a code in the backend that predicts whether a person is suffering from a brain tumor or not. Specificity, sensitivity, and accuracy are used to assess the performance of the suggested model.