Deep Learning Approaches for Automated Disease Classification From Medical Imaging Data

Anna Nowak

Deep Learning Approaches for Automated Disease Classification From Medical Imaging Data

Author(s)	Anna Nowak
Country	Poland
Abstract	Automated disease classification using medical imaging data has become an important research area within artificial intelligence, medical image analysis, and computational healthcare. Medical images contain complex anatomical and pathological information that requires careful interpretation to distinguish normal structures from abnormal findings. Conventional computational approaches frequently depend on manually designed image features, predefined classification rules, and domain-specific processing procedures. These requirements may restrict their adaptability when imaging characteristics vary across patients, acquisition systems, and clinical conditions. Deep learning techniques offer alternative approaches through hierarchical feature extraction and automated representation learning. The present paper develops a hypothetical methodological framework for examining deep learning approaches to automated disease classification using medical imaging data. Convolutional neural networks, transfer-learning models, residual neural architectures, and ensemble-based classification methods are considered. A fictional chest computed tomography dataset is used to illustrate the proposed methodology for classifying pulmonary lesions according to predefined benign and malignant categories. The study emphasizes image preprocessing, patient-level data partitioning, feature learning, model optimization, and structured performance assessment. Two illustrative numerical tables are presented to demonstrate how classification accuracy, sensitivity, specificity, macro-averaged F1-score, area under the receiver operating characteristic curve, and computational requirements could be compared. All numerical values are hypothetical and do not represent executed experiments or clinically validated findings. The methodological discussion considers the influence of image quality, annotation reliability, class imbalance, model complexity, and dataset variability. The paper identifies deep learning as a relevant methodological direction for medical image classification while emphasizing that reliable clinical application requires representative datasets, appropriate reference standards, independent external validation, and carefully evaluated prediction errors. The proposed framework is intended to support future experimental research rather than establish the diagnostic effectiveness of any particular computational model.
Keywords	Deep Learning, Medical Imaging, Automated Disease Classification, Convolutional Neural Networks, Computed Tomography, Transfer Learning, Artificial Intelligence, Medical Image Analysis.
Field	Engineering
Published In	Volume 1, Issue 1, March-April 2025
Published On	2025-03-31

View / Download PDF File

E-ISSN: 3067-5073

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJMLAIDSE DOI prefix is
10.00000/IJMLAIDSE

Downloads

Research Paper Format Copyright Permission and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJMLAIDSE Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	+1-872-302-3851	editor@uscse.com

International Journal of Machine Learning, AI & Data Science Evolution

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

Deep Learning Approaches for Automated Disease Classification From Medical Imaging Data

Share this