Machine-agnostic Automated Lumbar MRI Segmentation using a Cascaded Model Based on Generative Neurons

Automated lumbar spine segmentation is very crucial for modern diagnosis systems. In this study, we introduce a novel machine-agnostic approach for segmenting lumbar vertebrae and intervertebral discs from MRI images, employing a cascaded model that synergizes an ROI detection and a Self-organized Operational Neural Network (Self-ONN)-based encoder-decoder network for segmentation. Addressing the challenge of diverse MRI modalities, our methodology capitalizes on a unique dataset comprising images from 12 scanners and 34 subjects, enhanced through strategic preprocessing and data augmentation techniques. The YOLOv8 medium model excels in ROI extraction, achieving an excellent performance of 0.916 mAP score. Significantly, our Self-ONN-based model, combined with a DenseNet121 encoder, demonstrates excellent performance in lumbar vertebrae and IVD segmentation with a mean Intersection over Union (IoU) of 83.66%, a sensitivity of 91.44%, and Dice Similarity Coefficient (DSC) of 91.03%, as validated through rigorous 10-fold cross-validation. This study not only showcases an effective approach to MRI segmentation in spine-related disorders but also sets the stage for future advancements in automated diagnostic tools, emphasizing the need for further dataset expansion and model refinement for broader clinical applicability.

Self-DenseMobileNet: A Robust Framework for Lung Nodule Classification using Self-ONN and Stacking-based Meta-Classifier

In this study, we propose a novel and robust framework, Self-DenseMobileNet, designed to enhance the classification of nodules and non-nodules in chest radiographs (CXRs). Our approach integrates advanced image standardization and enhancement techniques to optimize the input quality, thereby improving classification accuracy. To enhance predictive accuracy and leverage the strengths of multiple models, the prediction probabilities from Self-DenseMobileNet were transformed into tabular data and used to train eight classical machine learning (ML) models; the top three performers were then combined via a stacking algorithm, creating a robust meta-classifier that integrates their collective insights for superior classification performance. To enhance the interpretability of our results, we employed class activation mapping (CAM) to visualize the decision-making process of the best-performing model. Our proposed framework demonstrated remarkable performance on internal validation data, achieving an accuracy of 99.28\% using a Meta-Random Forest Classifier. When tested on an external dataset, the framework maintained strong generalizability with an accuracy of 89.40\%. These results highlight a significant improvement in the classification of CXRs with lung nodules.

Deep Learning in Computed Tomography Pulmonary Angiography Imaging: A Dual-Pronged Approach for Pulmonary Embolism Detection

Pulmonary Embolism (PE) is a critical medical condition characterized by obstructions in the pulmonary arteries. Despite being a major health concern, it often goes underdiagnosed leading to detrimental clinical outcomes. The increasing reliance on Computed Tomography Pulmonary Angiography for diagnosis presents challenges and a pressing need for enhanced diagnostic solutions. The primary objective of this study is to leverage deep learning techniques to enhance the Computer Assisted Diagnosis of PE. This study presents a comprehensive dual-pronged approach combining classification and detection for PE diagnosis. We introduce an Attention-Guided Convolutional Neural Network (AG-CNN) for classification, addressing both global and local lesion region. For detection, state-of-the-art models are employed to pinpoint potential PE regions. Different ensembling techniques further improve detection accuracy by combining predictions from different models. Finally, a heuristic strategy integrates classifier outputs with detection results, ensuring robust and accurate PE identification. Our attention-guided classification approach, tested on the Ferdowsi University of Mashhad's Pulmonary Embolism (FUMPE) dataset, outperformed the baseline model DenseNet-121 by achieving an 8.1% increase in the Area Under the Receiver Operating Characteristic. By employing ensemble techniques with detection models, the mean average precision (mAP) was considerably enhanced by a 4.7% increase. The classifier-guided framework further refined the mAP and F1 scores over the ensemble models. Our research offers a comprehensive approach to PE diagnostics using deep learning, addressing the prevalent issues of underdiagnosis and misdiagnosis. We aim to improve PE patient care by integrating AI solutions into clinical workflows, highlighting the potential of human-AI collaboration in medical diagnostics.