Next, as shown in block 208, the process flow includes initiating one or more image bottleneck ensembles (IBE) algorithms on the one or more high-resolution images. In some embodiments, the one or more IBE algorithms may include one or more convolutional neural networks (CNN). In deep learning CNN in a class of deep neural networks, most commonly applied to analyzing visual imagery. A CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers of a CNN typically consist of a series of convolutional layers that convolve with a multiplication or other dot product. The activation function is commonly a ReLU layer, and is subsequently followed by additional convolutions such as pooling layers, fully connected layers and normalization layers, referred to as hidden layers because their inputs and outputs are masked by the activation function and final convolution. In some embodiments, the system may be configured to implement any of the following applicable CNN architectures either singly or in combination: Visual Geometry Group (VGG) 16, InceptionNet, ResNet50, Xception, InceptionResNetV3, ResNeXt50, WaveNet, and/or the like. Alternatively, any suitable deep CNN architectures can otherwise be incorporated in the system 130. Further, any suitable deep CNN architecture can be used in generating data relevant to the system 130.