Optimizing Medical Image Analysis: Leveraging Efficient Hardware and AI Algorithms

The surging use of medical AI algorithms and their hardware integration is transforming healthcare by improving non-invasive medical analysis with early disease detection, advanced segmentation, and classification. However, realizing comprehensive and accurate medical analysis through efficient AI-based tools necessitates a fundamental requirement — extensive multimodal data for training deep learning models. Handling this extensive data volume demands significant hardware resources, including multi-node training, to address the substantial computational requirements essential for accelerating model development. Hence, the challenge is two-fold: Achieving high accuracy while upholding a computationally inexpensive solution. To navigate this challenge, we propose a novel and efficient solution: a lightweight predictive tool for medical image classification developed by combining a Radiomics-based Random Forest model with MobileViT transformer, tailored for mobile applications. This approach ensures enhanced accuracy and reproducibility along with hardware flexibility. Our proposed method is exemplified by its superior performance in the BraTS2021 challenge, surpassing current state-of-the-art models with the best AUROC of 0.64 and 0.63 on both public and private test datasets respectively. The success of our approach highlights the potential of hybrid models in diverse medical applications beyond image classification.