## 1 Introduction  
The chikungunya virus (Chick V), a member of the Alphavirus genus and Togaviridae family [1], has been a global health concern since it was first identified in the United Republic of Tanzania in 1952 [2]. Over the past several decades, outbreaks have predominantly occurred in Africa and Asia, with the virus spreading to over 60 countries, leading to millions of infectious cases [2, 3]. In subsequent years, there were signs of a wider spread of infections [4], with reports in early 2000s of cases in Europe and the Americas, demonstrating the virus's adaptability [5]. Currently, according to reports, the chikungunya virus can be found in both tropical and subtropical regions [6]. The most found strains include the East Central South Africa (ECSA) strain and the Indian Ocean Lineage (IOL) strain [7; 8]. Chikungunya virus is primarily transmitted through the bites of Aedes aegypti and Aedes albopictus mosquitoes [9]. Upon infection, individuals typically develop chikungunya fever, a condition characterized by high fever, headaches, muscle pain, and severe joint inflammation [10] that can persist for weeks or even months, leading to joint deformities in severe cases [11]. Although chikungunya fever is rarely fatal, it can lead to significant morbidity, and in some instances, organ failure has been observed as a rare but severe complication [12]. Despite extensive research efforts over the years, no specific vaccine or antiviral therapy for chikungunya virus is currently available [13], and treatment remains limited to symptomatic relief [14]. This makes the development of new treatments and diagnostic methods an urgent priority as the number of cases continues to rise globally [15; 16]. Recent advancements in artificial intelligence and machine learning have shown great potential in the field of virology, especially in the detection of respiratory infections through medical imaging. For instance, the PulmoNet model demonstrated the potential of artificial intelligence in analyzing medical images for respiratory viral infections [17]. These advancements, while focused on respiratory viruses, suggest the potential for adaptation to other viral infections. For vector-borne diseases like chikungunya, innovative approaches combining Internet of Things (IoT) and machine learning technologies are being developed. A notable example is the MosquitoT system, which uses AI and image processing to monitor Aedes aegypti mosquitoes, the primary vectors for chikungunya and other viruses [18]. Although MosquitoT does not directly analyze viral particles, its integration of AI into entomological surveillance represents a crucial step toward improved disease control efforts. In addition, nanoparticle-based biosensors are being developed to detect chikungunya, marking another frontier in diagnostic technology that could be combined with image-based detection methods [19]. While these technologies show potential, a significant gap remains in the direct application of image processing techniques for chikungunya virus detection. Current efforts, such as the development of magnetic bead-based sample extraction coupled with polymerase spiral reaction, emphasize the need for rapid and accurate on-site diagnostic methods [20]. However, these approaches are not yet widely integrated with image processing technologies, leaving considerable room for further research into automated systems targeting chikungunya. In the biomedical field, image processing techniques have already made substantial contributions, particularly in viral infection detection [21]. High-throughput image analysis has become invaluable for screening large datasets, enabling rapid identification of infected cells in viral assays [22]. For viruses like dengue and Zika, automated image processing offers considerable advantages in speed and accuracy over traditional manual methods [23]. These improvements are particularly critical in drug discovery, where large libraries of compounds must be screened with high precision.