Snakebite envenomation is a pressing global health issue, causing numerous fatalities and disabilities each year. Prompt identification of venomous snakes is crucial for appropriate medical intervention. Traditional venom classification methods are time-consuming and invasive, highlighting the need for efficient and non-invasive approaches. Convolutional Neural Networks (CNNs), known for their image classification prowess, offer a promising solution. By training a CNN model on a diverse dataset of snake images, this study aims to develop a robust venom classification system. Such a system could revolutionize snakebite management, improving clinical outcomes and aiding in species conservation. The implementation of CNN-based venom classification has the potential to save lives, enhance healthcare decision-making, and protect endangered snake species.

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