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Gbeminiyi Ogunleye
7 months ago
Overview
Nigerian Researcher Develops Deep Learning Model To Transform Brain Cancer Diagnosis
In a groundbreaking development at the intersection of artificial intelligence (AI) and medicine, Tobi Titus Oyekanmi, a computer scientist and researcher at New Mexico Highlands University, has unveiled a deep learning model that could revolutionise the way brain cancer is diagnosed and interpreted particularly in resource-limited regions.
His study, titled “Deep Learning-Based Diagnosis of Brain Cancer Using Convolutional Neural Networks on MRI Scans: A Comparative Study of Model Architectures and Tumor Classification Accuracy,” introduces a novel LightBT-CNN system that achieved 98% diagnostic accuracy on MRI brain scans.
The research, published in the American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), positions Oyekanmi at the forefront of the emerging field of explainable artificial intelligence (XAI) for medical imaging.
Brain cancer remains one of the deadliest forms of cancer globally, with diagnosis often depending on the manual interpretation of MRI scans, a process that can be both time-consuming and prone to error.
On the goals, Oyekanmi said, “AI can help level the playing field. The goal was to build a lightweight yet powerful neural network that can analyse brain MRI scans with accuracy comparable to expert radiologists but without the need for expensive infrastructure.”
To achieve this, Oyekanmi led a multidisciplinary team comprising Peter Adigun, Nelson Azeez, and Ayodeji Adeniyi, developing the LightBT-CNN, a convolutional neural network designed to classify four tumor types (glioma, meningioma, pituitary, and healthy brain scans) using over 7,000 MRI images.
Unlike massive deep learning architectures such as VGG16 or ResNet50, which require high-end GPUs, Oyekanmi’s LightBT-CNN uses only 3.6 million trainable parameters, making it compact, cost-effective, and ideal for hospitals in developing countries.
