Image classification model for Retinal OCT Scans
Retinal optical coherence tomography (OCT) is a non-invasive imaging method employed for obtaining detailed cross-sectional images of the retina in living individuals. Developed an image classification models with TensorFlow and VGG-16 algorithm. The dataset utilized in this research is categorized into three directories: train, test, and val, designated for training, validation, and testing respectively. Each directory encompasses subfolders corresponding to distinct image categories, namely NORMAL, CNV, DME, and DRUSEN. Comprising a total of 10,300 X-ray images in JPEG format, the dataset is segregated into these four classes.