Abstract

The increasing demand for accurate and automated medical image classification has driven the development of hybrid artificial intelligence models that combine the strengths of deep learning and traditional machine learning techniques. Convolutional Neural Networks (CNNs) have proven highly effective in extracting deep spatial and semantic features from complex imaging data, while Support Vector Machines (SVMs) are renowned for their robustness in classification tasks, particularly in high-dimensional and limited-data environments. Integrating CNNs as feature extractors with SVMs as decision-making classifiers presents a promising paradigm for enhancing diagnostic performance in clinical settings. This chapter provides a comprehensive examination of CNN–SVM hybrid architectures, addressing key design principles, integration strategies, and their application across various medical imaging modalities including CT, MRI, and chest X-rays. Particular emphasis is placed on COVID-19 and lung disease classification, where rapid and precise image interpretation is critical. The study further explores technical challenges such as dimensionality reduction, model complexity, lack of standardization, and the need for interpretability in clinical environments. Empirical evidence from recent studies is analyzed to demonstrate the effectiveness of the hybrid approach in improving accuracy, sensitivity, and generalization. The chapter concludes with future research directions aimed at advancing the clinical translation of hybrid models for intelligent, scalable, and trustworthy medical imaging systems.

Introduction

The role of medical imaging in clinical diagnosis and decision-making has become increasingly critical with the advancement of radiological technologies [1]. Modalities such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and X-rays generate vast volumes of image data that require precise and timely interpretation [2]. The traditional reliance on manual analysis by radiologists, although effective, is both time-consuming and susceptible to variability in interpretation [3]. As a result, the integration of artificial intelligence (AI) into medical image classification systems has become a growing area of interest in both research and clinical domains. Among AI techniques, deep learning—particularly Convolutional Neural Networks (CNNs)—has demonstrated state-of-the-art performance in extracting hierarchical features from imaging data, facilitating the automation of classification tasks with high accuracy [4]. Their performance advantages, CNNs also face limitations, especially in scenarios with limited annotated data or overlapping class distributions, where conventional classification layers may not yield optimal decision boundaries [5].

Support Vector Machines (SVMs) have long been regarded as powerful classifiers within the machine learning paradigm, known for their generalization ability in high-dimensional feature spaces and their effectiveness with smaller datasets [6]. SVMs utilize a margin-based optimization approach to distinguish between classes, offering precise boundary definitions that are especially advantageous in medical contexts where misclassification can have serious clinical consequences [7]. SVMs lack the capacity to automatically extract features from raw image data, thus necessitating their combination with other methods for optimal performance [8]. This makes them an ideal candidate for hybrid modeling in conjunction with CNNs, where deep networks serve as feature extractors and SVMs operate as final-stage classifiers [9]. This complementary integration provides a strategic solution for enhancing classification accuracy, particularly in heterogeneous or data-scarce medical imaging applications [10].