Leveraging Transfer Learning Across Industrial and Medical Anomaly Domains: From Aircraft Fuselage Defect Detection to Chest X-ray Abnormality Identification (Published)
Transfer learning (TL) enables knowledge learned in one domain to improve performance in another related domain; however, its application to industrial and medical anomaly detection (AD) is often limited by domain discrepancies and scarce labeled data. This research addresses these challenges by bridging Aircraft Fuselage Defect Detection (AFDD) and Chest X-ray (CXR) Abnormality Identification through cross-domain TL, enabling effective feature generalization between industrial and medical imaging tasks. Publicly available aircraft inspection images and CXR datasets are used to ensure diversity and representative anomalies. Data pre-processing incorporates adaptive histogram equalization (AHE) for contrast enhancement and median filtering (MF) to reduce noise, followed by image normalization to standardize input dimensions. For robust feature representation, Gray-Level Co-occurrence Matrix (GLCM) and Histogram of Oriented Gradients (HOG) are employed to capture complementary structural and texture information. The proposed Dynamic Grey Wolf Optimizer-driven Deep Convolutional Transform Network (DGWO-DCTN) integrates preprocessing, feature extraction, and AD into a unified framework. A DC Neural Network (DCNN) extracts hierarchical spatial features, while a Transformer module models long-range dependencies and global contextual relationships. To improve convergence and generalization, the DGWO adaptively tunes network hyperparameters and weights. TL is realized by fine-tuning the model on CXR data using pretrained knowledge from fuselage defect detection. Experimental evaluation implemented in Python demonstrates strong performance, achieving 94% precision, 93.75% recall, 96.80% accuracy, and a 93.80% F1-score. These results confirm that combining TL with hybrid deep architectures provides an effective and computationally efficient solution for cross-domain AD.
Keywords: Computer Vision, abnormality identification, cross-domain, industrial inspection, medical imaging
Transformative Potential of Artificial Intelligence and Computer Vision in Modern Healthcare Diagnostics (Published)
Artificial intelligence and computer vision technologies are fundamentally transforming healthcare diagnostics and treatment through enhanced detection capabilities, improved accuracy, and revolutionary spatial precision. This comprehensive article examines five interconnected domains where computational intelligence is reshaping clinical practice: the diagnostic paradigm shift toward AI integration, machine learning algorithms for enhanced lesion detection across specialties, real-time analysis capabilities during procedures, robotic integration for unprecedented manipulation precision, and advanced spatial mapping technologies that revolutionize navigation within complex anatomy. The transformation demonstrates significant advancements in reducing diagnostic errors, minimizing inter-observer variability, improving treatment customization, enabling earlier detection of pathology, enhancing procedural safety, increasing precision of interventions, and facilitating remote healthcare delivery to underserved populations. Through the synergistic integration of human expertise with computational intelligence, these technologies collectively establish new standards for diagnostic and therapeutic capabilities while simultaneously addressing longstanding challenges in healthcare delivery. The evidence demonstrates that AI-augmented healthcare represents not merely an incremental improvement but rather a fundamental reconceptualization of how medical data is processed, analyzed, and translated into clinical decisions.
Keywords: Artificial Intelligence, Augmented Reality, Computer Vision, diagnostic accuracy, personalized medicine, robotic precision