Ability of Machine Learning and Deep Learning Models for Multiclass Classification of Kidney Stone and Lung Cancer from Computed Tomography Images - A Comparative Study

Aug 4, 2026

Feature extraction is crucial in biomedical image classification because it determines the accuracy of image representations and significantly impacts the effectiveness of classification models. Deep neural network architectures have gained attention for their ability to automatically extract important features, while traditional machine learning approaches have been comparatively overshadowed. This study compares three machine learning models (Gaussian Naïve Bayes, SVM, Random Forest) with three deep learning models (VGG16, InceptionV3, Xception) for multiclass classification using kidney stone and lung cancer CT datasets with four target classes each. Models are trained separately, deep learning uses transfer learning, and performance is evaluated with precision, recall and F1. Results show similar F1 scores across classes for both model families, suggesting comparable effectiveness. The findings support using machine learning to reduce training workload and computing requirements.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

1. Introduction

Feature extraction for biomedical image classification

Comparison of deep learning and traditional machine learning approaches

Motivation from kidney stone and lung cancer research

Defence Life Science Journal, Vol. 10, No. 1, January 2025, pp. 23-30, DOI : 10.14429/dlsj.19188 􀂔 2025, DESIDOC Ability of Machine Learning and Deep Learning Models for Multiclass Classification of Kidney Stone and Lung Cancer from Computed Tomography Images: A Comparative Study Anima Kujur* and Zahid Raza School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi-110 067, India. *Email: [anima.kujur55@gmail.com](anima.kujur55@gmail.com) ABSTRACT Feature extraction is crucial in biomedical image classification because it determines the accuracy of image representations and significantly impacts the effectiveness of classification models. Deep neural network classification architectures have gained significant interest due to their ability to automatically extract important features from input images, resulting in significant progress in diverse image classification tasks in recent years. However, with the rise of deep learning techniques, traditional machine learning approaches have been largely overshadowed. This study aims to close this gap by undertaking a rigorous comparative analysis of three important machine learning models, namely Gaussian Naïve Bayes, Support Vector Machine, and Random Forest Classifier, and three advanced deep learning models, namely VGG16, InceptionV3, and Xception. The comparison is based on their ability to do multiclass classification, using two datasets kidney stone and lung cancer. Each dataset consists of four different target classes. Both machine learning and deep learning frameworks are trained separately on the datasets, with deep learning models utilizing transfer learning techniques. The performance of each model across the varied output classes is assessed using evaluation measures such as precision, recall, and F1 scores. The results of the simulation analysis reveal that both machine learning and deep learning models perform equally well, as indicated by similar F1 scores across all output classes for both datasets. This study represents a major step towards simplifying classification efforts by promoting the use of machine learning models instead of deep learning models for classifying kidney stone and lung cancer datasets. This approach helps reduce the workload and computing requirements for training. Keywords: Deep learning; Machine learning; Biomedical image classification; Computed tomography; Biomedical image processing; Feature extraction 1. INTRODUCTION Accurate classification of biomedical images, particularly Computed Tomography (CT) scans, is vital for early detection and precise diagnosis of medical conditions such as kidney stones and lung cancer. Feature extraction, a fundamental technique in biomedical image classification 1,2, involves identifying and highlighting crucial image elements necessary for distinguishing between different disease classes . The effectiveness of any classification model heavily relies on the proficiency with which these features are extracted, directly impacting the accuracy and reliability of the classification process . Deep Neural Networks (DNNs)3,4 have emerged as formidable contendersin the realm of biological image classification due to their capability to autonomously learn intricate patterns from input images. These models have demonstrated outstanding performance across various image classification tasks, often surpassing traditional Machine Learning (ML)5 models . However, traditional ML techniques remain pertinent in Received : 06 June 2023, Revised : 27 September 2024 Accepted : 17 October 2024, Online published : 24 December 2024 biomedical image classification, offering results that are interpretable and computationally efficient, albeit facing challenges with complex datasets . Deep learning models, particularly DNNs, excel at detecting complex patterns and correlations within extensive and high-dimensional datasets . Their ability to learn directly from raw input data enables

FAQs

Which machine learning and deep learning models are compared in this study?

The study compares Gaussian Naïve Bayes, Support Vector Machine, and Random Forest with deep models VGG16, InceptionV3, and Xception.

How is multiclass classification evaluated for kidney stone and lung cancer?

Both datasets are trained separately, and model performance across output classes is assessed using precision, recall, and F1 scores.

What do the results indicate about machine learning versus deep learning?

Simulation results show that both approaches perform equally well, evidenced by similar F1 scores across all output classes for both datasets.

Aug 4, 2026

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