introduction to medical images classification using CNN

MICCAI 2024 MEC Submission5 Authors

Keywords: deep learning, CNN, medical images, classification, machine learning

Abstract: The application of Convolutional Neural Networks (CNNs) in medical image classification has revolutionized the field of medical diagnostics. CNNs, with their ability to automatically and adaptively learn spatial hierarchies of features from input images, have shown remarkable success in identifying and categorizing various medical conditions. This article explores the architecture and functioning of CNNs, highlighting their advantages over traditional image processing techniques. We delve into the most efficient CNN architectures, such as ResNet and Inception, and their specific applications in classifying medical images, including detection of pulmonary nodules, intracranial hemorrhages, and various cancers. Furthermore, we discuss the challenges faced in medical image classification, such as the need for large annotated datasets and the computational resources required. The article concludes with a discussion on the future prospects of CNNs in medical imaging, emphasizing the potential for improved diagnostic accuracy and the reduction of human error in medical practice.

Website: https://medium.com/@saraelhour5/introduction-to-medical-images-classification-using-cnn-c7aa51727669

Submission Number: 5