Attention and dilated convolutions inclusive deep-CNN with multiplexed texture features to diagnose Pathological and...

In today's era, precise and timely diagnosis of ocular diseases are crucial as these disorders jeopardize millions of visions. Early detection and proactive management can minimize vision threatening complications from these disorders. High Myopia(HM) and Pathological Myopia(PM), are the globally prevalent ocular diseases, which can impair vision acuity and productivity across all age groups. Routine screening via computer aided diagnosis(CAD) is required to detect HM and PM early and halt their progression. Previous studies majorly relied only on deep convolutional neural network(deep-CNN) features and independent analysis of PM and HM. This work seeks to provide a holistic analysis of PM and HM pathology by creating a novel ternary classifier on colour fundus images to detect normal vision, PM and HM respectively. We built the classifier by integrating texture features generated using gray level co-occurrence matrix(GLCM) within the deep-CNN model. Deep-CNN model comprises of spatial attention(SA) to locate lesion, squeeze-excitation(SE) to model interdependent channel attention & atrous or dilated convolutions to capture salient multiscale features of the related disease. Investigations employing extensive ablation techniques have elucidated the significance of optic disc(OD) and retinal vessel(RV) in the fundus images and their respective alterations in the HM or PM fundus. On the diverse dataset of 3212 colour fundus images, our ternary classifier has achieved 5-fold cross validation mean accuracy of 0.9754(+/-0.014), test accuracy of 0.9767 on 645 test fundus images and the kappa score of 0.9622 indicating the clinical viability of our classifier. The performance of our classifier excelled that of other pertinent studies for both PM and HM colour fundus images. Our classifier's test findings, comprehensive aetiology analysis and class activation maps are validated by the expert ophthalmologists making it reliable to serve as a virtual doctor alleviating the concerns with the lack of skilled ophthalmologists and expensive optometry tools by facilitating remote diagnostics and telemedicine applications.