Abstract

The rapid evolution of medical image processing has been driven by advancements in deep learning, enabling more accurate diagnostics, faster image analysis, and improved patient outcomes. Traditional image processing techniques often struggle with noise reduction, feature extraction, and segmentation, limiting their efficiency in complex medical imaging tasks. These limitations underscore the need for robust automated solutions that enhance diagnostic precision and reduce human error. Deep learning models, particularly convolutional neural networks (CNNs) and transformer-based architectures, have emerged as powerful tools for analyzing medical images. This study integrates deep learning methodologies to improve segmentation, classification, and anomaly detection across various imaging modalities, including MRI, CT scans, and ultrasound. A hybrid deep learning framework combining CNNs with attention mechanisms is proposed to enhance spatial feature extraction and contextual understanding. The model is trained on large-scale medical datasets, leveraging data augmentation and transfer learning to address challenges related to limited labeled data. Experimental results demonstrate significant improvements in classification accuracy, segmentation precision, and processing efficiency compared to conventional approaches. The proposed model achieves a classification accuracy of 98.5%, outperforming existing deep learning frameworks by 3–5%. Furthermore, segmentation performance, measured using Dice similarity coefficients, shows a 10% improvement over traditional methods.

Authors

S. Gomathi¹, R. Roopa Chandrika²
Muthayammal Engineering College, India¹, Karpagam Academy of Higher Education, India²

Keywords

Deep Learning, Medical Image Processing, Convolutional Neural Networks, Anomaly Detection, AI-Assisted Diagnostics