Published Paper Details:

Conjunctivitis, commonly known as pink eye, is a highly contagious eye condition that affects a large number of individuals worldwide. Timely and accurate detection of conjunctivitis, along with personalized drug recommendation, is crucial for effective treatment. In this study, we propose a novel system that combines convolutional neural networks (CNN) with advanced data analytics techniques to address this problem. The first component of our system focuses on conjunctivitis detection. We employ a CNN architecture to analyze digital images of the eye and classify them as either normal or indicative of conjunctivitis. The CNN model is trained on a large dataset of labeled eye images, enabling it to learn complex patterns and features associated with the disease. Through extensive experimentation and evaluation, we demonstrate the effectiveness of our CNN-based approach in accurately identifying conjunctivitis cases. The second component of our system aims to provide personalized drug recommendations based on the detected conjunctivitis condition. Leveraging the conjunctivitis diagnosis obtained from the CNN model, we employ data analytics techniques to analyze a comprehensive database of medications and their associated efficacy. By considering factors such as patient demographics, medical history, and drug interactions, our system generates personalized drug recommendations that optimize treatment outcomes and minimize adverse effects. To evaluate the performance of our system, we conducted experiments using real-world datasets comprising diverse eye images and patient profiles. The results demonstrate high accuracy in conjunctivitis detection and the generation of personalized drug recommendations. Our system shows great potential for assisting healthcare professionals in making informed decisions regarding conjunctivitis treatment, improving patient outcomes, and minimizing the spread of infection. The proposed Conjunctivitis Eye Detection and Personalized Drug Recommendation System using CNN combines state-of-the-art image analysis techniques with data analytics to provide accurate conjunctivitis detection and personalized drug recommendations. The system has the potential to enhance the efficiency and efficacy of conjunctivitis treatment, benefiting both healthcare professionals and patients.