Paper Title
Convolutional Neural Networks for Automated Brain Tumor Detection: A Transfer Learning Approach

Abstract
Brain tumor detection is one of the most important diagnostic problems in modern medicine, since timely and correct detection has a strong impact on the prognosis of patients, as well as their survival. Conventional diagnostic techniques mainly use invasive methods, especially tissue biopsies that pose considerable risks and delay the process of treatment. This paper outlines the design and testing of an AI enabled brain tumor recognition system based on Convolutional Neural Networks (CNNs), which is improved using transfer learning techniques. The studies have made use of magnetic resonance imaging (MRI) and computed tomography (CT) scan datasets that were made available publicly in repositories such as Kaggle and The Cancer Imaging Archive (TCIA). The study design is of high rigor through direct experimental design and stratified division of data to guarantee classes representation in each division, training, validation, and testing. The system architecture is based on multiple CNN models but because of transfer learning, the models can be adapted to the specialized domain, medical brain imaging. The method overcomes the general problem of a small amount of medical data but ensures the reliability of diagnosis. The described work develops a general basis of the automated analysis of medical images in detecting brain tumors, which makes it possible to realize the capabilities of non-invasively performing in the setting of the remote and supervised AI-based diagnostic algorithm. Future research directions follow the traces of scaling to bigger datasets, creating more complex model structures and to perform much more clinical validation studies to leave the bridge between computational research and clinical implementation