AYVANA: Deep Learning-Based Disease Diagnosis in Dermatology and Agriculture

Visual disease recognition in healthcare and agriculture involves learning discriminative image patterns across biologically distinct domains. While convolutional neural networks (CNNs) perform well in domain-specific tasks, most prior work relies on customized pipelines that limit architectural reuse. This work presents AYVANA, a consistent CNN-based framework with shared methodology and domain-specific optimization that evaluates the independent reuse of convolutional neural network (CNN) paradigm across dermatological and agricultural image-based disease classification tasks. Using established transfer learning, domain-specific pipelines are trained independently using domain-appropriate optimization strategies, while sharing a unified architectural paradigm, ensemble inference mechanism, evaluation, and interpretability approach. An EfficientNetB5–DenseNet169 ensemble achieves 85.61% accuracy and a macro F1-score of 0.814 on a ten-class dermatology dataset, while an EfficientNetB3–InceptionV3 ensemble attains 97.03% accuracy and a macro F1-score of 0.968 on a seventy-nine-class plant disease dataset. Soft-voting ensembles improve prediction consistency and balanced class-wise performance, and Grad-CAM visualizations provide qualitative insight. The results demonstrate that established CNN architectures can be independently retrained and reused across heterogeneous visual diagnostic under a consistent experimental protocol with shared evaluation and inference procedures, without shared representation learning or cross-domain feature transfer.