MACHINE LEARNING-BASED CANCER CLASSIFICATION IN MEDICAL IMAGING DATA

Abstract

Brain tumor classification from magnetic resonance imaging (MRI) data is crucial for accurate diagnosis and treatment planning in clinical practice. Accurate and timely tumor classification enables clinicians to determine appropriate treatment strategies, monitor disease progression, and assess treatment response effectively. Moreover, machine learning models can assist radiologists in interpreting MRI images, reducing interpretation errors and improving diagnostic accuracy. Additionally, brain tumor classification supports research efforts aimed at understanding tumor biology, identifying biomarkers, and developing targeted therapies for different tumor subtypes. Existing methods for brain tumor classification from MRI data often rely on manual segmentation and feature extraction, which can be labor-intensive and prone to variability. These methods may struggle to capture subtle differences in tumor morphology or texture, leading to inaccuracies in classification. Moreover, traditional approaches may require expertise in radiology and medical imaging, limiting their accessibility and scalability in clinical settings. Additionally, manual feature engineering may overlook important tumor characteristics or fail to exploit the full potential of MRI data for classification purposes. The proposed system utilizes machine learning techniques to automate and enhance brain tumor classification from MRI image data, addressing the limitations of existing methods. This work employs machine learning models to learn discriminative features directly from MRI images. By training models on large-scale MRI datasets annotated with tumor labels, the propsoed models can effectively differentiate between different tumor types and accurately classify brain tumors into relevant categories