Home / Current Issue / Paper 1717227
Malaria Detection Using an Improved AlexNet-Based Deep Learning Model
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence / Machine Learning
DOI: https://doi.org/10.64388/IREV9I11-1717227
Abstract
Diagnosing malaria still relies heavily on microscope examination of blood smears — which works, but is slow, hard to scale, and only as good as the person doing it. This paper describes a deep learning pipeline for binary malaria cell screening built on a modified AlexNet CNN. We used the public NIH/Kaggle malaria cell-image dataset, resized everything to 100×100 RGB patches, applied live augmentation, and trained a regularized CNN with batch normalization and dropout. Beyond just reporting accuracy, we evaluated the model with confusion matrices, ROC-AUC, precision-recall curves, calibration plots, and Grad-CAM heatmaps. We also compare the approach against a recent two-stage YOLOv4/DenseNet-121 system. This model doesn't do whole-slide detection or species identification — it's a clean, reproducible infected-vs-uninfected screener that can be understood, explained, and built on.
Keywords
Malaria Detection, Alexnet, Convolutional Neural Network, Grad-CAM, Medical Image Classification, Binary Screening.
References
[1] D. Sukumarran et al., "Automated Identification of Malaria-Infected Cells and Classification of Human Malaria Parasites Using a Two-Stage Deep Learning Technique," IEEE Access, vol. 12, 2024.
[2] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," NeurIPS, 2012.
[3] G. Huang et al., "Densely Connected Convolutional Networks," CVPR, 2017.
[4] R. R. Selvaraju et al., "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization," ICCV, 2017.
[5] I. Arunava, "Cell Images for Detecting Malaria," Kaggle Dataset.
[6] S. Rajaraman et al., "Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection," PeerJ, 2018.
How to cite this paper
@article{1717227,
author = {M. Phanindra, K. Venkata Siva Sai Sreeja, P. Nikhil},
title = {Malaria Detection Using an Improved AlexNet-Based Deep Learning Model},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {472-474},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717227.pdf},
abstract = {Diagnosing malaria still relies heavily on microscope examination of blood smears — which works, but is slow, hard to scale, and only as good as the person doing it. This paper describes a deep learning pipeline for binary malaria cell screening built on a modified AlexNet CNN. We used the public NIH/Kaggle malaria cell-image dataset, resized everything to 100×100 RGB patches, applied live augmentation, and trained a regularized CNN with batch normalization and dropout. Beyond just reporting accuracy, we evaluated the model with confusion matrices, ROC-AUC, precision-recall curves, calibration plots, and Grad-CAM heatmaps. We also compare the approach against a recent two-stage YOLOv4/DenseNet-121 system. This model doesn't do whole-slide detection or species identification — it's a clean, reproducible infected-vs-uninfected screener that can be understood, explained, and built on.},
keywords = {Malaria Detection, Alexnet, Convolutional Neural Network, Grad-CAM, Medical Image Classification, Binary Screening.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717227}
}