Deep Learning-Based Automated Detection of Lesions in Medical Imaging using Convolutional Neural Networks and Transfer Learning

The exponential growth of medical imaging data has created a significant demand for automated diagnostic tools capable of assisting radiologists in the detection and classification of pathological lesions. Traditional manual interpretation is labor-intensive, subject to interobserver variability, and prone to diagnostic errors due to fatigue. This paper presents a comprehensive study on the application of Deep Learning, specifically Convolutional Neural Networks, for the automated detection of lesions in medical imaging. Recognizing the inherent challenge of data scarcity in the medical domain, we employ Transfer Learning techniques to leverage the feature extraction capabilities of models pre-trained on large-scale natural image datasets. Our methodology involves the adaptation of established architectures, such as ResNet and VGG, which are fine-tuned on a curated dataset of annotated medical scans. We investigate the impact of various preprocessing strategies, data augmentation techniques, and hyperparameter configurations on the model performance. The experimental results demonstrate that the proposed approach achieves high sensitivity and specificity, significantly outperforming traditional computer-aided diagnosis systems based on hand-crafted features. The findings suggest that deep learning models, when effectively optimized via transfer learning, offer a robust and scalable solution for clinical decision support systems.