Comparative Analysis of Deep Learning for Image Processing and Classification

Modern image processing and classification methods utilizing deep learning, and defining the specific importance of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybridized models for high-dimensional, complex, and noisy image data. Examples of the application of these models are examined in applicative domains such as medical diagnostics,hyperspectral imaging, remote sensing, and the Internet of Medical Things (IoMT). Deep-Detect, CNN-RFD hybrids, and patch-based Deep Belief Networks can all deliver improved classification accuracy and speed of classification in real-time classification. Further intelligence can also be introduced in the form of intelligent preprocessing and optimization, as well as using reinforcement learning and evolutionary algorithms in conjunction with deep learning to develop decision making and classification solutions. The survey emphasizes the importance of prototyping scalable and interpretable solutions to real-world image classification problems, and offers suggestions on current and future research direction for those focused on building robust and efficient systems.