Neurojico-Driven Cognitive Image Classification Framework in Diagnosis Medical Imaging

Neurojico-managed cognitive image classification is a new innovative framework to classifying cognitive medical imaging, utilizing whole-body perceptual attributes of attention extracted from images. This Neurojico cognitive classification framework is designed based on cognitive neuroscience, combined with advanced deep learning techniques, to improve diagnostic accuracy. It employs a novel image cognitive classification system to identify new perceptual patterns for four attributes: attention, recency, variety, and adaptability, rather than simply focusing on individual pixel blocks. Neurojico mimics how the human brain processes information, enabling it to understand medical images more clearly and clinically relevantly. The system utilizes Resnet50 and compares the perceptual images between a comparison of tailored convolutional neural networks (CNNs) and pre-trained Resnet50 for feature extraction then making a classification based on logistic regression. In addition, it converts the feature vector into cognitive weighted feature vector and applying the classification based on Logistic Regression Test Accuracy to achieve 98.6%. These results indicate that Neurojico outperforms traditional models, such as CNNs and transformer-based systems, in terms of accuracy, sensitivity, and the precision with which it interprets decisions. These maps help radiologists better understand the rationale behind the whole-body cognitive model`s decisions, making the system more transparent and reliable.