An Intelligent method of analysis of Magnetic Resonance Images (MRI), X-Ray and CT Images for Abnormality Identification. - J. B., Kalaiah; Chandrashekara, S. N. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The efficient and accurate interpretation of biomedical imaging data, including Computed Tomography (CT), X-ray, and Magnetic Resonance Imaging (MRI), is crucial for the diagnosis, treatment planning, and management of various diseases. This study aims to develop and validate advanced computational models for the automated analysis of CT, X-ray, and MRI images to improve diagnostic accuracy and efficiency. By employing machine learning and deep learning techniques, our models are trained and tested on a comprehensive dataset of biomedical images to identify and classify pathological features across different conditions. In this work, we focus on the evaluation metrics of True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) rates, alongside derived statistical measures such as Sensitivity (Recall rate), Specificity, Precision, SNR and PSNR and the F1 score to assess the performance of our models. Sensitivity measures the model's ability to correctly identify positive cases, while Specificity assesses its ability to exclude negative cases accurately. Precision evaluates the proportion of true positive results in all positive predictions, and the F1 score provides a harmonic mean of Precision and Sensitivity, offering a balance between them for a comprehensive performance metric. Our findings demonstrate that the integration of proposed methodology significantly enhances the model's capability to accurately distinguish between pathological and non-pathological cases across CT, X-ray, and MRI modalities. The models exhibit high Sensitivity and Specificity, indicating reliable identification of disease presence and absence. Furthermore, the Precision and F1 scores highlight the models' accuracy and balanced performance in diagnostic predictions. The implications of this study are profound, offering a pathway towards the development of automated diagnostic tools that can support radiologists and healthcare practitioners in making more accurate, efficient, and consistent diagnostic decisions. By leveraging the quantitative analysis of TP, TN, FP, and FN rates, along with key performance metrics such as Sensitivity, Specificity, Precision, and F1 score, our research contributes to the ongoing efforts in improving patient outcomes through enhanced diagnostic imaging analysis.