Detecting Brain Tumor Stages Using Convolutional AutoEncoder (CAE) with Hybrid Deep Learning Method

The Brain Tumor (BT) is the formation of abnormal brain cells, few of which can develop into cancer. Early and prompt detection of disease and development of treatment strategies improve patients' wellbeing and life span. Models are created using Deep Learning (DL) and Magnetic Resonance Imaging (MRI) to classify and diagnose brain tumours. This facilitates the effortless detection of brain tumours. The BT is usually caused through abnormal brain cell proliferation that damage the brain structure as well as eventually resulted in various stages of BT. Early BT discovery as well as timely treatment have reduced the mortality rate. Consideration of deep structure for analysis that have been used in both non-linear feature extraction and unsupervised learning, which relies significantly on the Autoencoder (AE). This transfer learning is utilized to obtain better accuracy. Applications for the AE and its variations have been effective in many domains, including recommender systems, data production, pattern recognition, and computer vision. Moreover, the Convolutional AE (CAE) toolkits are addressed for better performance in detection of the brain tumor. The goal of this research is determining whether DL methods can be utilized to automate the detection process. This work uses Convolutional Neural Network (CNN) with VGG19 as the hybrid model to provide the better or maximum accuracy of 92.39% than the traditional Convolutional Neural Network (CNN) with VGG19. This study objective is to use TL with hybrid CNN techniques through this assessment and analysis to direct researchers and medical experts towards effective BT detection systems.