Explainable Deep Learning-Based Medical Diagnosis Assistant for Multi-Disease Detection and Clinical Decision Support

Abstract

Correct and prompt medical diagnosis is still a big challenge in modern healthcare especially for places with limited resources where there is a lack of specialists. This study proposes an Explainable Deep Learning Based Medical Diagnosis Assistant that can diagnose more accurately, lower the instances of false positives and false negatives, and help clinicians to make the right decisions for a variety of diseases such as cancer, tuberculosis, diabetic retinopathy, pneumonia, and neurological disorders. The suggested system harnesses convolutional neural networks and ensemble machine learning models coded in Python to both analyze medical images and clinical parameters that have been structured. A multi, stage pipeline including preprocessing, feature extraction, classification, and confidence scoring has been developed to furnish ranked differential diagnoses accompanied by visually explainable evidence. To improve the results on small medical datasets, transfer learning is used while calibration methods produce trustworthy probability scores for doctors to interpret. Moreover, the structure is suitable for telemedicine because it can be used for remote screening which makes it possible to have specialist, level support even in rural and less privileged areas. Experimental tests show marked enhancements in precision, recall, and inference time of diagnosis when compared to traditional machine learning methods.