Optical coherence tomography based diabetic - ophthalmic disease classification and measurement using bilateral filter and transfer learning approach

Optical Coherence Tomography (OCT) is a smooth application of low coherence interferometer with high air resolution and highly sensitive heterodyne detection technology to tomographic image measurement of living organisms. Currently, clinical applications are becoming more widespread in ophthalmology, cardiovascular system, dermatology, and dentistry. The problem with OCT is that the measurement area is as narrow as a few mm compared to other tomographic image measurement techniques, and it was initially applied to ophthalmology. Since then, various researches and developments have been carried out to expand clinical applications. Michelson type fiber optic interferometer is used for image acquisition. This paper presents a classification of ophthalmic diseases caused by diabetes. Bilateral filter is used for image pre-processing and noise removal. A transfer learning approach is implemented which uses AlexNet and Support vector machine (SVM) to classify the images. The AlexNet model is used to extract the features form the images and these features are classified using SVM model. The novelty of the proposed model lies in the use of image denoising using bilateral filter and then classification of the AlexNet features using SVM thereby achieving better classification accuracy with less training data. This also leads to better resource utilization. The ailments under study are Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), DRUSEN, and NORMAL. The proposed approach produced a higher classification accuracy of 99 % when compared to other deep learning algorithms like CNN, AlexNet and GoogleNet. The precision, sensitivity and specificity are recorded as 0.98, 0.99, and 0.99 respectively.