Innovations Item Code: IN-2024-2100399
Sector: Health/Telemedicine
Description:
Our innovation proposes the development and evaluation of a novel CNN-Transformer Artificial Intelligence architecture tailored for brain tumour classification in low-resource settings, particularly focusing on MRI-based diagnostics. By leveraging the power of deep learning, we integrate Convolutional Neural Networks (CNNs) and Visual Transformers (VTs) to harness both local and global image features, respectively. This hybrid approach aims to overcome the challenges of limited data and computational resources commonly encountered in resource-constrained healthcare environments.
Key Features
1. Hybrid CNN-Transformer Architecture: Integrating CNNs and VTs enables the model to capture both local and global features from MRI images, enhancing its ability to accurately classify brain tumours.
2.Transfer Learning Adaptation: Utilizing transfer learning, we leverage pre-trained CNN and VT models, fine-tuning them specifically for brain tumour classification tasks in low-resource settings. This approach optimizes model performance while reducing the need for extensive data and enormous computational resources for training from scratch.
3.Ensemble Techniques: We experiment and test diverse ensemble techniques such as boosting, stacking, and probability voting to further enhance classification accuracy and robustness. By combining multiple models, we mitigate individual model biases and errors, improving overall diagnostic reliability.
4.Operational Analysis: Our innovation also includes an operational analysis of computational resource demands, providing insights into the feasibility of deploying the proposed AI architecture in low-resource settings. This assessment encompasses considerations such as CPU, GPU, memory usage, and carbon footprint, ensuring practicality and sustainability in real-world applications.
Overall, our innovation represents a comprehensive approach to addressing the disparities in healthcare access and diagnostics quality prevalent in low-resource settings. By leveraging advanced AI techniques, our proposed CNN-Transformer architecture promises to deliver accurate, efficient, and resource-effective brain tumour classification; ultimately contributing to early disease diagnosis, improved patient outcomes and healthcare equity on a global scale.
Stage of Innovation: Proof of Concept (You have created something to show the innovation can work)
Problem:
The problem we aim to solve is the significant disparity in healthcare access and diagnostic accuracy for brain tumours in low-resource settings. In regions like Nigeria, where there's a shortage of skilled medical professionals, limited access to state-of-the-art equipment, and scant financial resources, timely and accurate diagnosis of brain tumours is often hindered. Manual analysis of MRI scans by radiologists is time-consuming, subjective, and dependent on expertise, leading to delayed diagnoses and inadequate treatments. Additionally, the rapid emigration of medical personnel exacerbates these challenges, further reducing access to specialized care.
Moreover, AI solutions developed in high-resource settings to tackle this problem often utilize traditional deep learning approaches for medical imaging classification; that typically requires large amounts of data and computational resources. This poses an unrealistic barrier of adoption in resource-constrained environments. While deep learning techniques have shown promise, their effectiveness in low-resource settings remains limited due to these constraints.
Our innovation addresses these challenges by proposing a novel CNN-Transformer AI architecture specifically designed for brain tumour classification in low-resource settings. By integrating CNNs and VTs, we aim to capture both local and global image features, enhancing diagnostic accuracy. Leveraging transfer learning, we adapt pre-trained models to the unique characteristics of MRI-based brain tumour classification tasks, reducing the need for extensive data and computational resources. Additionally, our exploration of various ensemble techniques further improves classification performance and robustness.
Through operational analysis, we assess the computational resource demands associated with deploying our proposed AI architecture, ensuring practicality and sustainability in real-world settings. By providing a comprehensive solution that combines advanced AI techniques with considerations for resource constraints, our innovation seeks to bridge the gap in healthcare access and diagnostics quality, ultimately leading to improved patient outcomes and healthcare equity in low-resource settings.
Unique Selling Point: Currently, in the field of medical imaging, including MRI-based brain tumour classification, researchers and practitioners primarily rely on traditional deep learning approaches, such as Convolutional Neural Networks (CNNs). These approaches often face challenges in low-resource settings due to their resource-intensive nature. Similarly, due to their inherent architecture, they provide utility in computer-aided diagnosis by only considering and limited local contexts (pixels) of data; an approach with many limitations, especially in fully “understanding” the data wholly. Our solution differs by proposing a hybrid CNN-Transformer architecture specifically tailored for brain tumour classification in low-resource settings. Unlike traditional CNNs, our architecture integrates Visual Transformers (VTs) alongside CNNs to capture both local and global image features, enhancing diagnostic accuracy. Moreover, we leverage transfer learning to adapt pre-trained models to the unique characteristics of MRI-based brain tumour classification tasks, reducing the need for extensive data and computational resources. Additionally, we explore ensemble techniques to further enhance classification performance and robustness. By combining multiple models, we mitigate individual model biases and errors, improving overall diagnostic reliability. Furthermore, our operational analysis considers the computational resource demands associated with deploying our solution, ensuring practicality and sustainability in real-world settings. Overall, our approach offers a comprehensive solution that addresses the challenges specific to low-resource healthcare environments, ultimately leading to improved healthcare access and diagnostics quality for brain tumours.