Using Convolutional Neural Networks for Accurate Medical Image Analysis

Abstract

Abstract— Convolutional Neural Networks (CNNs) have revolutionised medical image processing by making illness diagnosis faster and more accurate than ever before. This study delves into CNNs' use in medical imaging, showing how they may improve diagnostic accuracy while decreasing human error. We delve into the architecture and functioning of CNNs, emphasizing their suitability for processing complex medical images. Through a comprehensive review of existing literature, we demonstrate the effectiveness of CNNs in identifying and identifying anomalies in CT scans, MRI scans, and X-rays, among other types of medical pictures. Additionally, we present a case study showcasing the implementation of CNNs for detecting specific medical conditions, underscoring the improvements in accuracy and speed over traditional methods. The results affirm that CNNs, with their ability to learn and adapt, hold immense potential for advancing medical diagnostics and improving patient outcomes. Future research directions include optimizing CNN models for faster processing times and expanding their application to a broader range of medical conditions.