Automated cataract disease detection on anterior segment eye images using adaptive thresholding and fine tuned inception-v3 model

Early detection of cataracts plays a vital role in ensuring the prevention of vision loss. This paper aims to propose an algorithm that will act as an assistive measure in the process of automating cataract disease detection. The majority of the existing works are focused on the utilization of either fundus images, slit lamp images, or visible wavelength images captured using a DSLR camera. The novelty of this proposed algorithm is the capability to deliver equally accurate and precise performance using both normally captured visible wavelength images as well as medically captured anterior segment images which can, in turn, prove to be cost-effective as well. The image pre-processing techniques particularly adaptive thresholding hereby employed provides fast and accurate results on the input dataset fed to the CNN model which in turn is a fine-tuned version of Inception-v3. Here the training of the model has been done using visible wavelength images whereas the validation testing has been done using the anterior segment eye images medically obtained from a hospital. The proposed image preprocessing technique along with the model architecture ensures the achievement of a high classification accuracy of about 95%. Since the model deals with the examination of the anterior segment of the image, cases concerning nuclear cataract, cortical cataract, or a hybrid case involving the detection of both the aforementioned cataract types lie under the range of detection of our system.