Authors :

Faizan Sheikh and Sameer Patil

Address :

Department of Data Science, Aligarh Muslim University, India

Department of Machine Learning, Jamia Millia Islamia, India

Abstract :

Brain tumor characterization is vital for accurate diagnosis, treatment planning, and prognosis in oncology. Traditional imaging techniques like CT (Computed Tomography), MRI (Magnetic Resonance Imaging), and PET (Positron Emission Tomography) provide valuable information. Still, each modality has spatial resolution, sensitivity, or functional insight limitations. This reduces the precision of personalized treatment strategies and makes it challenging to assess treatment response and early detection. This study proposes a novel framework, namely MMIETC, integrating multi-modal imaging (MMI) data such as MRI, CT, PET using state-of-the-art methods for machine learning, including convolutional neural networks (CNNs) for automated feature extraction and tumor segmentation and random forests (RF) for Enhanced Tumor Characterization (ETC). Image processing algorithms like wavelet transforms will also be employed for enhanced segmentation and feature fusion. The study will focus on designing a unified computational framework that can accurately extract anatomical, functional, and molecular features from the imaging data, improving diagnostic precision. Integrating multi-modal imaging with CNNs for deep learning-based segmentation and random forests for predictive analysis is expected to yield significantly improved tumor characterization. The proposed approach should enhance tumor margin delineation, detect intra-tumor heterogeneity, and identify biomarkers for more accurate diagnostic and prognostic evaluation. These advancements will lead to better treatment planning, more personalized therapies, and improved patient outcomes in oncology.

Keywords :

Multi-model imaging, machine learning techniques, convolutional neural networks, random forest, feature extraction, biomarker identification, feature fusion.