Mohammad Manzurul Islam

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About

Mohammad Manzurul Islam is a researcher at the forefront of applying artificial intelligence to solve pressing real-world challenges, with a particular focus on medical imaging and sustainable technology. His work bridges computer vision and practical applications, demonstrating how deep learning can transform both healthcare diagnostics and environmental management. In his highly cited 2023 study, "Unmasking Ovary Tumors: Real-Time Detection with YOLOv5," Islam pioneered a rapid, cost-effective approach to identifying ovarian growths using the YOLOv5 object detection framework, addressing critical limitations of traditional ultrasonic imaging. This work, which has already garnered 5 citations, offers a promising pathway toward faster and more accessible tumor screening. Expanding his impact into sustainability, Islam developed "An extensive photographic dataset to classify laptop components for automating e-waste management by recycling old laptops" in 2024. This dataset, cited 2 times, provides a foundational resource for training AI-enabled robots to efficiently sort and recycle electronic parts from diverse laptop models. By creating this open resource, Islam directly supports the automation of e-waste processing, a vital step toward reducing environmental harm. His research exemplifies a commitment to leveraging AI for tangible societal benefit, from saving lives through early detection to building a greener future.