Enhancing Hydatid Cyst Classification with Deep Learning and Convolutional Neural Networks Using CT Scans

Abstract

Background: Hydatid cysts, caused by Echinococcus granulosis, are a serious health concern with potential complications. Traditional diagnostic methods, like clinical examination and imaging interpretation, can be subjective and error-prone. Artificial Intelligence and Deep Learning techniques can revolutionize healthcare by enhancing disease detection and diagnosis, with the study focusing on precise detection and classification. Methods: A Convolutional Neural Network (CNN) model was developed, utilizing image preprocessing techniques to accurately classify hydatid cysts in Computed Tomography (CT) scans. Training relied on a curated dataset, enabling the model to learn and identify key patterns indicative of hydatid cyst presence and its stage detection in CT scan images. Result: The AI model employed in this study achieved a 90% accuracy in classifying hydatid cyst stages using CT scan images. By providing essential information about the cyst stage, healthcare professionals can accurately inform patients based on CT scan analysis. Conclusion: The study explores the use of AI and DL in hydatid cyst stage classification using a CNN model trained on CT scan images. The approach aims to reduce hydatid cyst growth rates by aiding in early detection, highlighting the significant transformation in the healthcare industry due to advancements in disease detection, diagnosis, and treatment.