A Survey of Deep Learning-Based Pattern Classification Frameworks for Medical and Agricultural Image Analysis

Disease classification from images plays a central role in decision support in today's world of medicine and agriculture, and scientists working on the problem in these two disciplines developed their solutions almost independently. The paper provides a survey of ten deep learning approaches to disease classification (2016-2025), five in medicine and five in plant sciences, focusing on the four key aspects: architecture, dataset, performance reported, and limitations. While benchmark classification accuracy is now close to 97-99% level, the actual generalization is not well tested, computational requirements far exceed those of edge devices, and interpretability is an option. None of the ten surveyed papers evaluated their model on data from the other discipline even though they use exactly the same set of visual features for classification - texture abnormalities, irregular boundaries, and gradient color changes. Based on this gap analysis, this paper presents the idea of a general and unified approach in computer vision which is architecture agnostic framework it could be implemented using any deep learning or any hybrid deep learning method and operates seamlessly in both domains without the need for complete retraining