Brain tumor prediction and analysis based on simple CNN

Abstract

The field of brain tumor prediction aims to detect potentially malignant tumors before overt symptoms appear. Early tumor detection can help tailor treatment pathways. To this end, this paper introduces a convolutional neural network (CNN) to construct a simple CNN analysis model. Subtle architectural changes to typical CNN designs are aimed at navigating and solving complex problems unique to medical imaging. Specifically, first, a preprocessing stage converts raw Magnetic Resonance Imaging (MRI) scans into analyzable data. This enables the model to delve into complex patterns in each image. Second, build a simple CNN model and evaluate it. Additionally, comparisons are made with established benchmarks set by traditional CNN architectures. The study further utilizes many available annotated datasets to develop a data augmentation paradigm that improves model resilience and generalizability. At the same time, the study deploys rigorous protocols to address and contain the ubiquitous overfitting threat, thereby enhancing the general applicability of the solution across a variety of healthcare settings. Empirical evaluations shed light on the significant superiority of the proposed simple CNN, especially in specific imaging environments and data structures. At the heart of this research is not only to expand technological horizons but also to reimagine clinical diagnostics to improve the standard of patient care.