Ocular disease examination of fundus images by hybriding SFCNN and rule mining algorithms

Eye is the organ of vision, which is necessary for daily routine, disorders can prevent it from working effectively. Human eye can be harmed, including age, strain, poor care, and using medications without a prescription. Choroid behind the retina are affected by retinal disorders. Small and big blood vessels compressed densely and affected by light, in order to perform multi-label categories of fundus images and properly handle the issue of interleaved overflow in fundus image lesions. We proposed Single population leapfrog optimization convolutional neural network algorithm (SFCNN) and Rule mining algorithm for self-closing computer investigation of ocular disease related to retinal images, which makes the work easier for ophthalmologists to state the state of the disease. For classification hybrid techniques SFCNN and EARMAM are proposed to predict the diseased image. When comparing with existing approach, proposed approach yields an accuracy of 98.75%, a sensitivity of 97%, and specificity of 98%.