DEEP LEARNING TECHNIQUES FOR MEDICAL IMAGE ANALYSIS

Abstract

Medical image analysis is critical for accurate diagnosis and treatment planning in
healthcare. Deep learning, a subset of artificial intelligence, has revolutionized this field by
providing automated, accurate, and efficient methods for analyzing complex medical images
such as MRI, CT scans, X-rays, and ultrasound images. This article reviews the state-of-the
art deep learning techniques applied in medical image analysis, including convolutional
neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial
networks (GANs). Challenges such as data scarcity, interpretability, and computational
demands are discussed. The article also highlights ongoing research and applications within
Pakistan’s medical research community. Two graphs illustrate the comparative accuracy of
deep learning models and the growth of related research publications.