Academic Research & Scholarly Contributions
This research presents an ensemble approach using multiple deep convolutional neural network architectures for accurate classification of human skin diseases. The proposed method demonstrates improved performance compared to individual models.
This study develops a mobile-compatible deep convolutional neural network model for efficient detection and classification of maize leaf diseases. The solution enables farmers to identify crop diseases using smartphone cameras for timely intervention.
This study proposes a novel approach combining the feature extraction capability of the VGG16 and ResNet-50 deep learning algorithms with the precise segmentation power of the U-Net architecture.
This study introduces a customized MobileNetV2 (CMBNV2) lightweight model for the early detection of Mpox, which delivers excellent detection performance while maintaining efficiency.