Enhancing Medical Image Processing and Analysis with Machine Learning Techniques

Abstract

Computer vision and image analysis rely heavily on machine learning. From structure-from-motion and object recognition to scene understanding and image segmentation and registration, there is a wide spectrum of problems. Information may be extracted from visual data using machine learning algorithms. A rapidly expanding area of deep learning is medical picture analysis. Standard machine learning (ML) methods from computer vision, ML models from deep learning, and medical image analysis are all examples of DL approaches and their uses in this area. Classifying things, such lesions, into specific classes using input parameters like contrast and area obtained from segmented objects is one of the most recent uses of ml in computer-aided diagnosis and medical image analysis. An artificial neural network is conceptually based on a neural system. Convolutional neural networks (CNNs) and neural filters are at the heart of deep learning. Deep learning and other forms of ML that take images as input are highly effective and valuable technologies. In most cases, a medical specialist will be the one to make the interpretation of the medical data. Due to factors such as subjectivity, visual complexity, interpreter weariness, and large fluctuations, human specialists have a hard time interpreting images. Adding to deep learning's impressive track record of success in the real world, it has introduced novel, highly accurate solutions for medical imaging. For potential uses in healthcare in the future, it is considered a crucial technique.