DEEP LEARNING-BASED COMPUTER VISION FOR AUTOMATED DETECTION AND CLASSIFICATION OF DISEASES FROM MEDICAL IMAGES

Abstract

Medical imaging has turned out to be crucial to modern diagnosis; yet conventional image interpretation remains reliant on on specialist expertise, time, and clinical workload. Deep learning-based computer vision has emerged as a promising approach for automated detection and classification of diseases from X-rays, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, retinal images, and histopathological images. This study investigates organizational perceptions of the effectiveness, usefulness, implementation readiness, and challenges associated with deep learning-based medical image analysis in healthcare organizations in Lahore, Pakistan. A quantitative, cross-sectional survey design was adopted. For the purpose of this research draft, an illustrative dataset of 240 respondents from hospitals, diagnostic laboratories, medical technology organizations, and healthcare-related academic/research institutions in Lahore was developed. Data were analyzed using descriptive statistics, Pearson correlation, and multiple regression. The illustrative findings indicate strong organizational perceptions of the diagnostic value of deep learning, particularly for early disease detection, image classification, workflow support, and reduction of diagnostic workload. Respondents also identified data quality, model interpretability, cyber security, infrastructure costs, shortage of AI expertise, and concerns about clinical accountability as major barriers. Regression results suggest that perceived diagnostic accuracy, organizational readiness, technological infrastructure, and clinician trust are positively associated with intention to adopt deep learning-based computer vision. The study concludes that deep learning can substantially strengthen medical image analysis in Lahore's healthcare sector, but successful implementation requires locally representative datasets, clinical validation, human oversight, staff training, ethical governance, and sustainable technological infrastructure.