Detection of pneumonia from chest X-rays using convolutional neural networks
DOI: https://doi.org/10.3846/mla.2025.23905Abstract
Pneumonia detection from chest X-rays is crucial for early diagnosis, and deep learning models –specifically convolutional neural networks (CNNs) – have shown promise in automating this process. In this study, a CNN using the DenseNet-121 architecture was developed and trained, referred to as LDCS2, to classify chest X-ray images as pneumonia or normal, using a combined dataset from three publicly available sources. The CNN approach was chosen over Vision Transformers (ViT) due to lower computational requirements and better performance with limited data. A traditional training, validation, and testing split was used instead of k-fold cross-validation to reduce execution time. LDCS2 demonstrated excellent discrimination between pneumonia and normal images alongside high computational efficiency. These findings highlight the potential of DenseNet-based CNNs for automated pneumonia diagnosis, particularly in resource-constrained settings.
Article in English.
Pneumonijos nustatymas iš krūtinės ląstos rentgenogramų, naudojant konvoliucinius neuroninius tinklus
Santrauka
Pneumonijos nustatymas iš krūtinės ląstos rentgenogramų yra itin svarbus ankstyvajai diagnostikai, o giliojo mokymosi modeliai – ypač konvoliuciniai neuroniniai tinklai (CNN) – rodo didelį potencialą automatizuojant šį procesą. Šiame tyrime sukurtas ir apmokytas CNN, paremtas DenseNet-121 architektūra ir pavadintas LDCS2, skirtas klasifikuoti krūtinės ląstos rentgeno vaizdams, iš kurių matyti pneumonija arba sveiki plaučiai, naudojant sujungtą duomenų rinkinį iš trijų viešai prieinamų šaltinių. CNN metodas pasirinktas vietoje Vision Transformers (ViT) dėl mažesnių skaičiavimo išteklių reikalavimų ir geresnių rezultatų, kai duomenų kiekis ribotas. Siekiant sutrumpinti vykdymo laiką, vietoje k kartų kryžminės validacijos taikytas tradicinis mokymo, validacijos ir testavimo skaidymas. LDCS2 pademonstravo puikią atskyrimo gebą tarp pneumonijos ir sveikų plaučių vaizdų bei aukštą skaičiavimo efektyvumą. Šie rezultatai pabrėžia DenseNet pagrindu veikiančių CNN potencialą automatizuotai plaučių uždegimo diagnostikai, ypač išteklių stokojančiose aplinkose.
Reikšminiai žodžiai: LDCS2, konvoliuciniai neuroniniai tinklai (CNN), krūtinės ląstos rentgeno vaizdų klasifikavimas, pneumonijos aptikimas, DenseNet-121, medicininis vaizdavimas, gilusis mokymasis sveikatos priežiūros srityje, duomenų augmentacija, perkėliminis mokymasis.
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LDCS2, Convolutional Neural Networks (CNNs), chest X-ray classification, pneumonia detection, DenseNet-121, medical imaging, deep learning in healthcare, data augmentation, transfer learningHow to Cite
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Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
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Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
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