Exploration of Machine Learning and Deep Learning Approaches in Medical Domain

Abstract

Through the combination of machine learning (ML) and deep learning (DL) approaches, substantial progress has been made in the field of medical picture categorization, which is an essential component in the field of medical diagnostics. Within the context of medical picture categorization, this paper provides an in-depth examination of the development, methodology, and applications of machine learning and deep learning. By making use of handmade features, traditional machine learning techniques, such as support vector machines and decision trees, have laid the groundwork for early achievements in the field. On the other hand, the introduction of deep learning, and more specifically convolutional neural networks (CNNs), has brought about a revolution in the industry by making it possible to automatically extract features and obtaining greater performance. This article takes a look at a number of different deep learning architectures, including ResNet, VGG, and Inception, and highlights the contributions that these designs have made to tasks such as illness categorization, organ segmentation, and tumor identification. In addition to this, it discusses alternative solutions such as data augmentation, transfer learning, and model optimization after addressing problems such as the lack of data, the interpretability of the data, and the demands placed on the computing resources. In addition, the evaluation takes into account the ethical concerns, as well as the need for rigorous validation in order to guarantee clinical application. This study highlights the revolutionary influence that machine learning and deep learning have had on medical imaging by conducting a comparative analysis of current research. It also highlights the ongoing need for innovation and cooperation across disciplines in order to improve diagnostic accuracy and patient outcomes.