Traditional CNNs

Traditional Convolutional Neural Networks (CNNs) are a cornerstone of computer vision, aiming to extract hierarchical features from images for tasks like classification, segmentation, and object detection. Current research focuses on improving CNN performance through architectural innovations (e.g., DenseNet, EfficientNet, and variations incorporating adaptive convolution layers) and leveraging transfer learning to adapt pre-trained models to specific applications. The widespread use of CNNs in diverse fields, from medical image analysis to automated systems for malaria detection and biometric security, highlights their significant impact on both scientific understanding and practical applications.