Pathologic myopia diagnosis and localization from retinal fundus images using custom CNN

Pathologic myopia (PM) is the critical factor of irreversible visual artifacts and puts patients at risk of other severe retinal diseases such as glaucoma. Early intervention can help control the disease's progression and prevent vision loss. Due to its prevalence worldwide, automated detection of PM and its severity is essential. Deep learning-aided diagnosis has proven itself in the field of ophthalmology. The proposed study automatically classifies pathologic and non-pathologic myopia from the fundus images using a guided mini U-Net (GM-U-Net) for feature extraction integrated with a customized convolutional neural network (PMNet) explicitly designed for fundus images. The proposed GM-U-Net allows a deeper network with significantly reduced parameters than conventional U-Net for feature extraction. The proposed PMNet utilizes the features extracted by GM-U-Net to discriminate between PM and a normal retina image. The PMNet classification performance is compared with the other transfer learning models based on the features provided by the GM-U-Net. The combination of GM-U-Net and PMNet outperforms the different models for PM classification. In-depth ablation tests are conducted to realize the current form of PMNet and test its effectiveness. PMNet achieves an accuracy of 90%, average sensitivity of 93%, and specificity of 97% for binary class on the test set, demonstrating it as a valuable tool for early PM detection. Further, to localize the prominent regions in the images, colored heatmap techniques are applied to visualize the affected areas with a hotter color.