Interpreting Deep Convolutional Neural Networks for Medical Diagnosis Using Perceptual Encoding and Retrieval

The robust performance of deep neural networks (DNNs) in many areas, including medical diagnosis, is not accessible from the problem of interpretability. Even though DNNs have high accuracy, they tend to operate as black boxes, usually without any explanation of which basis their predictions are made. This interpretability issue has become important, especially for high-stakes applications, such as medical image analysis and disease diagnosis, in which people’s lives are at risk. The study was structured to raise novel approaches for DNNs, internal representation translation and provide visualizations to assist medical diagnosis tasks and ensure reliable and transparent decision-making procedures. Such techniques have been developed to improve the interpretability of DNN without sacrificing the performance. The models for proposed models succeeded in the clinical tasks of medical diagnosis. They reached competitive accuracy with the best model, obtaining an accuracy of 95.7% and an F1-score of 0.914 on lung abnormality detection from chest X-rays. AUC-ROC high sensitivity and specificity of 0.963 were observed when diagnosing brain tumors based on MRI scans. These models also provided improved faithfulness perceived through saliency maps, which had an average score of 0.82, and concept activation vectors, with a consistency of 0.77 from the evaluation done by medical experts. The suggested methods offer a chance for a better patient outcome via high-precision and safe diagnostics and treatment options with an expected change in misdiagnosis rate of 10-15%.