DEEP LEARNING–BASED FRAMEWORK FOR AUTOMATED DETECTION AND LOCALIZATION OF MALARIA IN MICROSCOPIC BLOOD SMEAR IMAGES

Hooria Khan; Muhammad Qasim Khan; Hamail Raza Zaidi; Tauseef Noor; Abdul Aziz

doi:10.71146/kjmr834

Authors

Hooria Khan Lecturer at Iqra National University Peshawar Author
Muhammad Qasim Khan Associate Professor School of CS INU Peshawar Author
Hamail Raza Zaidi Software Engineering HOP Iqra National University Peshawar Author
Tauseef Noor City University of Science and Information Technology Author
Abdul Aziz Lab Instructor at Iqra National University Peshawar Author

DOI:

https://doi.org/10.71146/kjmr834

Keywords:

Malaria Detection, Deep Learning, CNN, YOLOv8, Blood Smear Images, Medical Image Analysis

Abstract

Malaria remains a major global health challenge, particularly in developing regions where timely and accurate diagnosis is critical. Conventional microscopic examination of blood smears is labor-intensive, time-consuming, and highly dependent on expert interpretation, leading to potential diagnostic errors. Recent advances in deep learning have enabled automated malaria detection with promising accuracy; however, most existing approaches are limited to image-level classification and lack parasite localization capability.

This paper proposes a robust deep learning framework that integrates Convolutional Neural Network (CNN)-based classification with YOLOv8-based object detection for accurate detection and localization of malaria-infected regions in microscopic blood smear images. The CNN model effectively distinguishes infected and healthy blood cells, achieving an accuracy of 97.19%. To overcome the limitations of classification-only models, YOLOv8 is employed to localize malaria parasites within blood cells, resulting in an improved accuracy of 98% along with precise spatial detection. Experimental results demonstrate that the proposed framework enhances diagnostic accuracy, interpretability, and clinical applicability, offering an efficient and reliable solution for automated malaria diagnosis.

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