MOGONET: Evolving Deep Learning by Multi-Objective Generalized Normal Distribution Optimization for Conjunctivitis Identification

Abstract Conjunctivitis is one of the most common eye disorders, and timely diagnoses are crucial to ensure no complications arise from such disorders. Manually examining the images to diagnose the disease often takes too much time and may result in inconsistent diagnosis. To overcome this problem, this paper presents a framework referred to as MOGONET, which is based on deep learning (DL) techniques and involves image preprocessing, image segmentation, data augmentation, transfer learning, and multi-objective optimization for diagnosing conjunctivitis eye disease. To increase the quality of the image for analysis, image contrast enhancements are employed. Furthermore, image multi-threshold segmentation techniques are used to identify vital areas of the eye. Also, an advanced Generalized Normal Distribution Optimization (GNDO) algorithm combined with a CNN is used to improve the efficiency and minimize computational complexity. The proposed model is tested using conjunctivitis eye image dataset from Kaggle and Shutterstock. According to the result findings, the accuracy, precision, recall, and F-score of the proposed model is 98.32%, 97.54%, 96.92%, and 96.22%, respectively. Moreover, the model is highly efficient as it resulted in minimized loss, floating-point operation, and fewer trainable parameters than some popular DL frameworks.