Detection of Malaria Infections Using Convolutional Neural Networks

Ñahui Vargas, Luis Edison

Detection of Malaria Infections Using Convolutional Neural Networks

dc.contributor.advisor	Aquino Cruz, Mario
dc.contributor.author	Ñahui Vargas, Luis Edison
dc.date.accessioned	2025-10-05T01:58:48Z
dc.date.available	2025-10-05T01:58:48Z
dc.date.issued	2025-09-03
dc.description.abstract	Malaria persists as a serious global public health threat, particularly in resource-limited regions where timely and accurate diagnosis is a challenge due to poor medical infrastructure. This study presents a comparative evaluation of three pre-trained convolutional neural network (CNN) architectures—EfficientNetB0, InceptionV3, and ResNet50—for automated detection of Plasmodium-infected blood cells using the Malaria Cell Images Dataset. The models were implemented in Python with TensorFlow and trained in Google Colab Pro with GPU A100 acceleration. Among the models evaluated, ResNet50 proved to be the most balanced, achieving 97% accuracy, a low false positive rate (1.8%) and the shortest training time (2.9 hours), making it a suitable choice for implementation in real-time clinical settings. InceptionV3 obtained the highest sensitivity (98% recall), although with a higher false positive rate (4.0%) and a higher computational demand (6.5 hours). EfficientNetB0 was the fastest model (3.2 hours), showed validation and a higher false negative rate (6.2%). Standard metrics—accuracy, loss, recall, F1- score and confusion matrix—were applied under a non- experimental cross-sectional design, along with regularization and data augmentation techniques to improve generalization and mitigate overfitting. As a main contribution, this research provides reproducible empirical evidence to guide the selection of CNN architectures for malaria diagnosis, especially in resource- limited settings. This systematic comparison between state-of-the- art models, under a single protocol and homogeneous metrics, represents a significant novelty in the literature, guiding the selection of the most appropriate architecture. In addition, a lightweight graphical user interface (GUI) was developed that allows real-time visual testing, reinforcing its application in clinical and educational settings. The findings also suggest that these models, in particular ResNet50, could be adapted for the diagnosis of other parasitic diseases with similar cell morphology, such as leishmaniasis or babesiosis.
dc.format	application/pdf
dc.identifier.uri	https://hdl.handle.net/20.500.14195/398
dc.language.iso	spa
dc.publisher	Universidad Nacional Micaela Bastidas de Apurímac
dc.publisher.country	PE
dc.rights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	Malaria diagnosis
dc.subject	CNN architectures
dc.subject	Deep learning
dc.subject	Plasmodium
dc.subject	Clinical decision support
dc.subject	Medical imaging
dc.subject.ocde	https://purl.org/pe-repo/ocde/ford#1.02.00
dc.title	Detection of Malaria Infections Using Convolutional Neural Networks
dc.type	info:eu-repo/semantics/bachelorThesis
dc.type.version	info:eu-repo/semantics/publishedVersion
renati.advisor.dni	41202588
renati.advisor.orcid	https://orcid.org/0000-0002-2552-5669
renati.author.dni	71958460
renati.discipline	612296
renati.juror	Ibarra Cabrera, Manuel Jesús
renati.juror	Rojas Enríquez, Hesmeralda
renati.juror	Quispe Merma, Rafael Ricardo
renati.level	https://purl.org/pe-repo/renati/nivel#tituloProfesional
renati.type	https://purl.org/pe-repo/renati/type#tesis
thesis.degree.discipline	Ingeniería Informática y Sistemas
thesis.degree.grantor	Universidad Nacional Micaela Bastidas de Apurímac. Facultad de Ingeniería
thesis.degree.name	Ingeniero Informático y Sistemas