Abstract: Detection of Brain Tumor Through Retina proposes a non-invasive method for early brain tumour detection using retinal imaging. Since brain abnormalities often affect the retina, featureslike papilledema and optic atrophy are analyzed using fundus and OCT images. A deep learning model is trained to detect these signs, with key features like disc swelling and nerve fibre thinning extracted automatically. A web- based interface enables clinicians to upload retinal images and receive real-time diagnostic predictions. This approach offers a cost-effective, accessible alternative to traditional brain imaging, aiding in early diagnosis and intervention.
The system leverages convolutional neural networks (CNNs) to achieve high accuracy in identifying visual biomarkers. It incorporates Grad-CAM heatmaps to enhance model interpretability for clinicians. Retinal datasets are pre-processed for quality enhancement and standardized input. The model undergoes extensive validation using labeled clinical datasets. Predictions are supplemented with confidence scores to support clinical decision-making. This innovative framework bridges ophthalmology and neurology, transforming retinal scans into a powerful diagnostic tool.
Keywords: Brain Tumor Detection; Retinal Imaging; Papilledema; Deep Learning; Fundus Image Analysis; Non-Invasive Screening; Convolutional Neural Networks
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DOI:
10.17148/IJARCCE.2025.141212
[1] Mrs R S Geethanjali, Enturi Lokesh, Gonuguntla Prashanth Kumar, Gorthi Yaswanth and Likhitha P V, "Detection of Brain Tumour Through Retina: A Modern Approach to Brain Tumor Detection," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.141212
