Medical image reader powered by artificial intelligence

Abstract

Misdiagnosis in medical imaging is a critical concern, risking patients’ health due to the pivotal role of radiologists’ accuracy in diagnostics. Current cross-checking methods for radiologists’ decisions are limited, potentially leading to errors and treatment delays. This study introduces a data processing technique and an advanced prediction system for improving disease detection accuracy in medical images. Our main goal is to contribute to healthcare by developing a system capable of achieving human-level or higher accuracy in disease detection across diverse medical image types. To achieve this, we utilize deep learning techniques, specifically Convolutional Neural Networks (CNNs), and leverage Transfer Learning with pre-trained models. Data processing plays a crucial role, given the importance of image availability and quality. We apply image enhancement techniques such as Histogram Equalization, Adaptive Histogram Equalization (AHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image quality and augment a limited training dataset. The advanced ensemble approach significantly enhances the overall accuracy and reduces individual model variance. Validation of our approach using confusion matrices reveals that selective class-wise voting achieves the highest accuracy at 95.27% on the testing dataset. Additionally, our customized weighted voting approach achieves an accuracy of 94.07% on the test set. These results emphasize the effectiveness of our ensemble techniques in improving disease detection accuracy. Our ensemble techniques offer substantial accuracy improvements, promising more accurate and reliable medical diagnoses