Published Paper Details:

The majority of the population is significantly affected by brain tumours, which are a serious health risk. A primary brain tumour, which can be malignant or non-cancerous, affects 5 out of every 100,000 people each year. The prevention of the disease's progression and the improvement of patient outcomes can both be facilitated by early diagnosis of brain tumours. In this study, we provide a novel deep convolutional neural network (DCNN)-based method for the early diagnosis of brain tumours. The suggested approach entails pre-processing brain tumor-containing and tumor-free MRI scan pictures, followed by the use of DCNNs to automatically detect the existence of a tumour in the MRI data. The DCNNs have a 79% accuracy after being trained on the VGG16 and Inception V3 architectures. The technology streamlines the processes and human interventions necessary for diagnosis, thereby enhancing the effectiveness and efficiency of medical personnel and experts. The promise of DCNNs in the realm of medical imaging, particularly for the detection and management of brain tumours, is illustrated in this research.