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Thabang Isaka

Project Title

Leveraging Attention-Integrated Ensemble Deep Learning for Enhanced Malaria Diagnosis through Thick and Thin Blood Smear Microscopy

Project Description

Malaria diagnosis is a critical yet challenging task, especially in resource-limited settings where access to skilled professionals is restricted. The conventional diagnostic process involves the scrutiny of both thick and thin blood smear microscopy images, a labour-intensive task demanding a high level of expertise. Even as AI solutions have begun to aid this process, a significant gap remains; the prevailing frameworks predominantly focus on the analysis of thin blood smears, overlooking the rich information that thick smears can provide. This project seeks to bridge this gap, laying the groundwork for a comprehensive solution that harmonizes the analysis of both thick and thin smears through an attention-integrated ensemble deep learning framework, thereby promising a more robust and detailed diagnostic outcome. Objective To pioneer and validate a framework that fully leverages the synergy of ensemble deep learning and attention mechanisms, aiming to automate the intricate process of malaria diagnosis. This initiative envisages a tool that can keenly analyse both thick and thin smear microscopy images, spotlighting critical areas and extracting nuanced details to enhance the accuracy and depth of malaria diagnosis.

Proposed Approach Phase 1: Undertake a meticulous literature review to grasp the depth of the current diagnostic landscape and discern the unaddressed needs in the analysis of thick blood smears.

Phase 2: Develop a prototype framework that brings together various deep learning models, weaving in attention mechanisms to accentuate critical regions in both thick and thin smear microscopy images, fostering a holistic diagnostic approach.

Phase 3: Validate the framework employing a diverse dataset of microscopy images, working towards refining the system to function as a comprehensive diagnostic tool that stands tall in real-world scenarios.

Phase 4: Collaborate with healthcare professionals to fine-tune the system based on real-world feedback, ensuring it is primed to serve as a reliable aid in malaria diagnosis, especially in settings lacking skilled professionals.

Expected Outcomes

• A validated framework capable of revolutionizing malaria diagnosis by offering a comprehensive analysis tool that accommodates both thick and thin smear microscopy images, filling a crucial gap in current diagnostic approaches.

• A significant stride in making malaria diagnosis more accessible and accurate, particularly in remote areas, presenting a tool that can function efficiently with minimal expert intervention (Especially in Africa since Malaria is a major problem that is experienced at a large scale).