CuchiaWoundDB: Image Dataset for Wound Healing Analysis in Monopterus cuchia

The Monopterus cuchia is a commercially important aquaculture species high in omega-3 and omega-6 fatty acids, which are necessary for wound healing. Advances in moist wound dressing and bioactive compounds in Monopterus cuchia extracts provide intriguing insights into deep learning-based wound healing investigations, which require a solid dataset. The paper proposes an image dataset for Monopterus cuchia. The dataset contains images of wound healing development in Monopterus cuchia. Fishes use wound healing to grow and survive in their aquatic environments. Aquatic animals have unique physiological systems that enable them to heal wounds quickly. This dataset was created to enable machine learning-based analysis of wound healing development, which is a critical area of biomedical research for the identification of tissue regeneration pathways and the design of therapeutic interventions. Knowledge on wound healing is important for finding out the factors that affect healing time, enhancing treatment methods, and developing automated diagnostic systems. Images were taken at different intervals to monitor morphological changes and healing patterns. The deep learning models may be employed on this dataset for wound healing classification, segmentation, and analysis. This dataset provides images of wound healing in Monopterus cuchia under two conditions: normal repair and retinoic acid treatment. The data is organized into two main folders, each containing a set of subfolders for different experimental conditions at various time points. This comprehensive dataset can be utilized in early detection of wounds, healing and classification in Cuchia fish.